torchgeo.datasets¶
In torchgeo
, we define two types of datasets: Geospatial Datasets and Non-geospatial Datasets. These abstract base classes are documented in more detail in Base Classes.
Geospatial Datasets¶
GeoDataset
is designed for datasets that contain geospatial information, like latitude, longitude, coordinate system, and projection. Datasets containing this kind of information can be combined using IntersectionDataset
and UnionDataset
.
Dataset |
Type |
Source |
Size (px) |
Resolution (m) |
---|---|---|---|---|
Masks |
Landsat, LiDAR |
40,000x40,000 |
30 |
|
Masks |
Aster |
3,601x3,601 |
30 |
|
Geometries |
Bing Imagery |
|||
Imagery, Masks |
NAIP |
1 |
||
Masks |
Remote Sensing, In Situ Measurements |
3 |
||
Masks |
Aerial |
30 |
||
Points |
Citizen Scientists |
|||
Imagery, Masks |
NAIP, NLCD, OpenStreetMap |
1 |
||
Masks |
Sentinel-2 |
10 |
||
Masks |
Aster, SRTM, Russian Topomaps |
25 |
||
Points |
Citizen Scientists |
|||
Masks |
Landsat |
45,000x45,000 |
100 |
|
Points |
Citizen Scientists |
|||
Imagery |
Landsat |
8,900x8,900 |
30 |
|
Imagery |
Aerial |
6,100x7,600 |
1 |
|
Geometries |
Maxar, CNES/Airbus |
|||
Imagery |
Sentinel |
10,000x10,000 |
10 |
Aboveground Woody Biomass¶
- class torchgeo.datasets.AbovegroundLiveWoodyBiomassDensity(root='data', crs=None, res=None, transforms=None, download=False, cache=True)¶
Bases:
RasterDataset
Aboveground Live Woody Biomass Density dataset.
The Aboveground Live Woody Biomass Density dataset is a global-scale, wall-to-wall map of aboveground biomass at ~30m resolution for the year 2000.
Dataset features:
Masks with per pixel live woody biomass density estimates in megagrams biomass per hectare at ~30m resolution (~40,000x40,0000 px)
Dataset format:
geojson file that contains download links to tif files
single-channel geotiffs with the pixel values representing biomass density
If you use this dataset in your research, please give credit to:
New in version 0.3.
- __init__(root='data', crs=None, res=None, transforms=None, download=False, cache=True)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
cache (bool) – if True, cache file handle to speed up repeated sampling
- Raises
FileNotFoundError – if no files are found in
root
Aster Global DEM¶
- class torchgeo.datasets.AsterGDEM(root='data', crs=None, res=None, transforms=None, cache=True)¶
Bases:
RasterDataset
Aster Global Digital Evaluation Model Dataset.
The Aster Global Digital Evaluation Model dataset is a Digital Elevation Model (DEM) on a global scale. The dataset can be downloaded from the Earth Data website after making an account.
Dataset features:
DEMs at 30 m per pixel spatial resolution (3601x3601 px)
data collected from the Aster instrument
Dataset format:
DEMs are single-channel tif files
New in version 0.3.
- __init__(root='data', crs=None, res=None, transforms=None, cache=True)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found, here the collection of individual zip files for each tile should be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if dataset is missing
Canadian Building Footprints¶
- class torchgeo.datasets.CanadianBuildingFootprints(root='data', crs=None, res=1e-05, transforms=None, download=False, checksum=False)¶
Bases:
VectorDataset
Canadian Building Footprints dataset.
The Canadian Building Footprints dataset contains 11,842,186 computer generated building footprints in all Canadian provinces and territories in GeoJSON format. This data is freely available for download and use.
- __init__(root='data', crs=None, res=1e-05, transforms=None, download=False, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (float) – resolution of the dataset in units of CRS
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if
download=False
and data is not found, orchecksum=True
and checksums don’t match
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Return type
Changed in version 0.3: Method now takes a sample dict, not a Tensor. Additionally, it is possible to show subplot titles and/or use a custom suptitle.
Chesapeake Land Cover¶
- class torchgeo.datasets.Chesapeake(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
RasterDataset
,ABC
Abstract base class for all Chesapeake datasets.
Chesapeake Bay High-Resolution Land Cover Project dataset.
This dataset was collected by the Chesapeake Conservancy’s Conservation Innovation Center (CIC) in partnership with the University of Vermont and WorldView Solutions, Inc. It consists of one-meter resolution land cover information for the Chesapeake Bay watershed (~100,000 square miles of land).
For more information, see:
- __init__(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if
download=False
but dataset is missing or checksum fails
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Return type
Changed in version 0.3: Method now takes a sample dict, not a Tensor. Additionally, possible to show subplot titles and/or use a custom suptitle.
- class torchgeo.datasets.Chesapeake7(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
Complete 7-class dataset.
This version of the dataset is composed of 7 classes:
No Data: Background values
Water: All areas of open water including ponds, rivers, and lakes
Tree Canopy and Shrubs: All woody vegetation including trees and shrubs
Low Vegetation: Plant material less than 2 meters in height including lawns
Barren: Areas devoid of vegetation consisting of natural earthen material
Impervious Surfaces: Human-constructed surfaces less than 2 meters in height
Impervious Roads: Impervious surfaces that are used for transportation
Aberdeen Proving Ground: U.S. Army facility with no labels
- class torchgeo.datasets.Chesapeake13(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
Complete 13-class dataset.
This version of the dataset is composed of 13 classes:
No Data: Background values
Water: All areas of open water including ponds, rivers, and lakes
Wetlands: Low vegetation areas located along marine or estuarine regions
Tree Canopy: Deciduous and evergreen woody vegetation over 3-5 meters in height
Shrubland: Heterogeneous woody vegetation including shrubs and young trees
Low Vegetation: Plant material less than 2 meters in height including lawns
Barren: Areas devoid of vegetation consisting of natural earthen material
Structures: Human-constructed objects made of impervious materials
Impervious Surfaces: Human-constructed surfaces less than 2 meters in height
Impervious Roads: Impervious surfaces that are used for transportation
Tree Canopy over Structures: Tree cover overlapping impervious structures
Tree Canopy over Impervious Surfaces: Tree cover overlapping impervious surfaces
Tree Canopy over Impervious Roads: Tree cover overlapping impervious roads
Aberdeen Proving Ground: U.S. Army facility with no labels
- class torchgeo.datasets.ChesapeakeDC(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
This subset of the dataset contains data only for Washington, D.C.
- class torchgeo.datasets.ChesapeakeDE(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
This subset of the dataset contains data only for Delaware.
- class torchgeo.datasets.ChesapeakeMD(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
This subset of the dataset contains data only for Maryland.
Note
This dataset requires the following additional library to be installed:
zipfile-deflate64 to extract the proprietary deflate64 compressed zip file.
- class torchgeo.datasets.ChesapeakeNY(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
This subset of the dataset contains data only for New York.
Note
This dataset requires the following additional library to be installed:
zipfile-deflate64 to extract the proprietary deflate64 compressed zip file.
- class torchgeo.datasets.ChesapeakePA(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
This subset of the dataset contains data only for Pennsylvania.
- class torchgeo.datasets.ChesapeakeVA(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
This subset of the dataset contains data only for Virginia.
Note
This dataset requires the following additional library to be installed:
zipfile-deflate64 to extract the proprietary deflate64 compressed zip file.
- class torchgeo.datasets.ChesapeakeWV(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
Chesapeake
This subset of the dataset contains data only for West Virginia.
- class torchgeo.datasets.ChesapeakeCVPR(root='data', splits=['de-train'], layers=['naip-new', 'lc'], transforms=None, cache=True, download=False, checksum=False)¶
Bases:
GeoDataset
CVPR 2019 Chesapeake Land Cover dataset.
The CVPR 2019 Chesapeake Land Cover dataset contains two layers of NAIP aerial imagery, Landsat 8 leaf-on and leaf-off imagery, Chesapeake Bay land cover labels, NLCD land cover labels, and Microsoft building footprint labels.
This dataset was organized to accompany the 2019 CVPR paper, “Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data”.
The paper “Resolving label uncertainty with implicit generative models” added an additional layer of data to this dataset containing a prior over the Chesapeake Bay land cover classes generated from the NLCD land cover labels. For more information about this layer see the dataset documentation.
If you use this dataset in your research, please cite the following paper:
- __getitem__(query)[source]¶
Retrieve image/mask and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of image/mask and metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data', splits=['de-train'], layers=['naip-new', 'lc'], transforms=None, cache=True, download=False, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
splits (Sequence[str]) – a list of strings in the format “{state}-{train,val,test}” indicating the subset of data to use, for example “ny-train”
layers (Sequence[str]) – a list containing a subset of “naip-new”, “naip-old”, “lc”, “nlcd”, “landsat-leaf-on”, “landsat-leaf-off”, “buildings”, or “prior_from_cooccurrences_101_31_no_osm_no_buildings” indicating which layers to load
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if
download=False
but dataset is missing or checksum failsAssertionError – if
splits
orlayers
are not valid
Global Mangrove Distribution¶
- class torchgeo.datasets.CMSGlobalMangroveCanopy(root='data', crs=None, res=None, measurement='agb', country='AndamanAndNicobar', transforms=None, cache=True, checksum=False)¶
Bases:
RasterDataset
CMS Global Mangrove Canopy dataset.
The CMS Global Mangrove Canopy dataset consists of a single band map at 30m resolution of either aboveground biomass (agb), basal area weighted height (hba95), or maximum canopy height (hmax95).
The dataset needs to be manually dowloaded from the above link, where you can make an account and subsequently download the dataset.
New in version 0.3.
- __init__(root='data', crs=None, res=None, measurement='agb', country='AndamanAndNicobar', transforms=None, cache=True, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
measurement (str) – which of the three measurements, ‘agb’, ‘hba95’, or ‘hmax95’
country (str) – country for which to retrieve data
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if dataset is missing or checksum fails
AssertionError – if country or measurement arg are not str or invalid
Cropland Data Layer¶
- class torchgeo.datasets.CDL(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
RasterDataset
Cropland Data Layer (CDL) dataset.
The Cropland Data Layer, hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth.
If you use this dataset in your research, please cite it using the following format:
- __init__(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 after downloading files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if
download=False
but dataset is missing or checksum fails
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Return type
Changed in version 0.3: Method now takes a sample dict, not a Tensor. Additionally, possible to show subplot titles and/or use a custom suptitle.
EDDMapS¶
- class torchgeo.datasets.EDDMapS(root='data')¶
Bases:
GeoDataset
Dataset for EDDMapS.
EDDMapS, Early Detection and Distribution Mapping System, is a web-based mapping system for documenting invasive species and pest distribution. Launched in 2005 by the Center for Invasive Species and Ecosystem Health at the University of Georgia, it was originally designed as a tool for state Exotic Pest Plant Councils to develop more complete distribution data of invasive species. Since then, the program has expanded to include the entire US and Canada as well as to document certain native pest species.
EDDMapS query results can be downloaded in CSV, KML, or Shapefile format. This dataset currently only supports CSV files.
If you use an EDDMapS dataset in your research, please cite it like so:
EDDMapS. YEAR. Early Detection & Distribution Mapping System. The University of Georgia - Center for Invasive Species and Ecosystem Health. Available online at https://www.eddmaps.org/; last accessed DATE.
Note
This dataset requires the following additional library to be installed:
pandas to load CSV files
New in version 0.3.
- __getitem__(query)[source]¶
Retrieve metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data')[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
- Raises
FileNotFoundError – if no files are found in
root
ImportError – if pandas is not installed
EnviroAtlas¶
- class torchgeo.datasets.EnviroAtlas(root='data', splits=['pittsburgh_pa-2010_1m-train'], layers=['naip', 'prior'], transforms=None, prior_as_input=False, cache=True, download=False, checksum=False)¶
Bases:
GeoDataset
EnviroAtlas dataset covering four cities with prior and weak input data layers.
The EnviroAtlas dataset contains NAIP aerial imagery, NLCD land cover labels, OpenStreetMap roads, water, waterways, and waterbodies, Microsoft building footprint labels, high-resolution land cover labels from the EPA EnviroAtlas dataset, and high-resolution land cover prior layers.
This dataset was organized to accompany the 2022 paper, “Resolving label uncertainty with implicit generative models”. More details can be found at https://github.com/estherrolf/qr_for_landcover.
If you use this dataset in your research, please cite the following paper:
New in version 0.3.
- __getitem__(query)[source]¶
Retrieve image/mask and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of image/mask and metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data', splits=['pittsburgh_pa-2010_1m-train'], layers=['naip', 'prior'], transforms=None, prior_as_input=False, cache=True, download=False, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
splits (Sequence[str]) – a list of strings in the format “{state}-{train,val,test}” indicating the subset of data to use, for example “ny-train”
layers (Sequence[str]) – a list containing a subset of
valid_layers
indicating which layers to loadtransforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
prior_as_input (bool) – bool describing whether the prior is used as an input (True) or as supervision (False)
cache (bool) – if True, cache file handle to speed up repeated sampling
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if
download=False
but dataset is missing or checksum failsAssertionError – if
splits
orlayers
are not valid
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
Note: only plots the “naip” and “lc” layers.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if the NAIP layer isn’t included in
self.layers
- Return type
Esri2020¶
- class torchgeo.datasets.Esri2020(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)¶
Bases:
RasterDataset
Esri 2020 Land Cover Dataset.
The Esri 2020 Land Cover dataset consists of a global single band land use/land cover map derived from ESA Sentinel-2 imagery at 10m resolution with a total of 10 classes. It was published in July 2021 and used the Universal Transverse Mercator (UTM) projection. This dataset only contains labels, no raw satellite imagery.
The 10 classes are:
No Data
Water
Trees
Grass
Flooded Vegetation
Crops
Scrub/Shrub
Built Area
Bare Ground
Snow/Ice
Clouds
A more detailed explanation of the invidual classes can be found here.
If you use this dataset please cite the following paper:
New in version 0.3.
- __init__(root='data', crs=None, res=None, transforms=None, cache=True, download=False, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if
download=False
but dataset is missing or checksum fails
EU-DEM¶
- class torchgeo.datasets.EUDEM(root='data', crs=None, res=None, transforms=None, cache=True, checksum=False)¶
Bases:
RasterDataset
European Digital Elevation Model (EU-DEM) Dataset.
The EU-DEM dataset is a Digital Elevation Model of reference for the entire European region. The dataset can be downloaded from this website after making an account. A dataset factsheet is available here.
Dataset features:
DEMs at 25 m per pixel spatial resolution (~40,000x40,0000 px)
vertical accuracy of +/- 7 m RMSE
data fused from ASTER GDEM, SRTM and Russian topomaps
Dataset format:
DEMs are single-channel tif files
If you use this dataset in your research, please give credit to:
New in version 0.3.
- __init__(root='data', crs=None, res=None, transforms=None, cache=True, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found, here the collection of individual zip files for each tile should be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
GBIF¶
- class torchgeo.datasets.GBIF(root='data')¶
Bases:
GeoDataset
Dataset for the Global Biodiversity Information Facility.
GBIF, the Global Biodiversity Information Facility, is an international network and data infrastructure funded by the world’s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth.
This dataset is intended for use with GBIF’s occurrence records. It may or may not work for other GBIF datasets. Data for a particular species or region of interest can be downloaded from the above link.
If you use a GBIF dataset in your research, please cite it according to:
Note
This dataset requires the following additional library to be installed:
pandas to load CSV files
New in version 0.3.
- __getitem__(query)[source]¶
Retrieve metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data')[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
- Raises
FileNotFoundError – if no files are found in
root
ImportError – if pandas is not installed
GlobBiomass¶
- class torchgeo.datasets.GlobBiomass(root='data', crs=None, res=None, measurement='agb', transforms=None, cache=True, checksum=False)¶
Bases:
RasterDataset
GlobBiomass dataset.
The GlobBiomass dataset consists of global pixel wise aboveground biomass (AGB) and growth stock volume (GSV) maps.
Dataset features:
estimates of AGB and GSV around the world at ~100m per pixel resolution (45,000x45,0000 px)
standard error maps of respective measurement at same resolution
Dataset format:
estimate maps are single-channel
standard error maps are single-channel
The data can be manually downloaded from this website.
If you use this dataset please cite it with the following citation:
Santoro, M. et al. (2018): GlobBiomass - global datasets of forest biomass. PANGAEA, https://doi.org/10.1594/PANGAEA.894711
New in version 0.3.
- __getitem__(query)[source]¶
Retrieve image/mask and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample at index consisting of measurement mask with 2 channels, where the first is the measurement and the second the error map
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data', crs=None, res=None, measurement='agb', transforms=None, cache=True, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
measurement (str) – use data from ‘agb’ or ‘gsv’ measurement
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
RuntimeError – if dataset is missing or checksum fails
AssertionError – if measurement argument is invalid, or not a str
iNaturalist¶
- class torchgeo.datasets.INaturalist(root='data')¶
Bases:
GeoDataset
Dataset for iNaturalist.
iNaturalist is a joint initiative of the California Academy of Sciences and the National Geographic Society. It allows citizen scientists to upload observations of organisms that can be downloaded by scientists and researchers.
If you use an iNaturalist dataset in your research, please cite it according to:
Note
This dataset requires the following additional library to be installed:
pandas to load CSV files
New in version 0.3.
- __getitem__(query)[source]¶
Retrieve metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data')[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
- Raises
FileNotFoundError – if no files are found in
root
ImportError – if pandas is not installed
Landsat¶
- class torchgeo.datasets.Landsat(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
RasterDataset
,ABC
Abstract base class for all Landsat datasets.
Landsat is a joint NASA/USGS program, providing the longest continuous space-based record of Earth’s land in existence.
If you use this dataset in your research, please cite it using the following format:
If you use any of the following Level-2 products, there may be additional citation requirements, including papers you can cite. See the “Citation Information” section of the following pages:
- __init__(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
bands (Sequence[str]) – bands to return (defaults to all bands)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
- Raises
FileNotFoundError – if no files are found in
root
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if the RGB bands are not included in
self.bands
- Return type
Changed in version 0.3: Method now takes a sample dict, not a Tensor. Additionally, possible to show subplot titles and/or use a custom suptitle.
- class torchgeo.datasets.Landsat9(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat8
Landsat 9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS).
- class torchgeo.datasets.Landsat8(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat
Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS).
- class torchgeo.datasets.Landsat7(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat
Landsat 7 Enhanced Thematic Mapper Plus (ETM+).
- class torchgeo.datasets.Landsat5TM(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat4TM
Landsat 5 Thematic Mapper (TM).
- class torchgeo.datasets.Landsat5MSS(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat4MSS
Landsat 4 Multispectral Scanner (MSS).
- class torchgeo.datasets.Landsat4TM(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat
Landsat 4 Thematic Mapper (TM).
- class torchgeo.datasets.Landsat4MSS(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat
Landsat 4 Multispectral Scanner (MSS).
- class torchgeo.datasets.Landsat3(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Landsat1
Landsat 3 Multispectral Scanner (MSS).
NAIP¶
- class torchgeo.datasets.NAIP(root, crs=None, res=None, transforms=None, cache=True)¶
Bases:
RasterDataset
National Agriculture Imagery Program (NAIP) dataset.
The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.
NAIP is administered by the USDA’s Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This “leaf-on” imagery is used as a base layer for GIS programs in FSA’s County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.
If you use this dataset in your research, please cite it using the following format:
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Return type
Changed in version 0.3: Method now takes a sample dict, not a Tensor. Additionally, possible to show subplot titles and/or use a custom suptitle.
Open Buildings¶
- class torchgeo.datasets.OpenBuildings(root='data', crs=None, res=0.0001, transforms=None, checksum=False)¶
Bases:
VectorDataset
Open Buildings dataset.
The Open Buildings dataset consists of computer generated building detections across the African continent.
Dataset features:
516M building detections as polygons with centroid lat/long
covering area of 19.4M km2 (64% of the African continent)
confidence score and Plus Code
Dataset format:
csv files containing building detections compressed as csv.gz
meta data geojson file
The data can be downloaded from here. Additionally, the meta data geometry file also needs to be placed in root as tiles.geojson.
If you use this dataset in your research, please cite the following technical report:
New in version 0.3.
- __getitem__(query)[source]¶
Retrieve image/mask and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of image/mask and metadata for the given query. If there are not matching shapes found within the query, an empty raster is returned
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data', crs=None, res=0.0001, transforms=None, checksum=False)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (float) – resolution of the dataset in units of CRS
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
FileNotFoundError – if no files are found in
root
Sentinel¶
- class torchgeo.datasets.Sentinel(root, crs=None, res=None, transforms=None, cache=True)¶
Bases:
RasterDataset
Abstract base class for all Sentinel datasets.
Sentinel is a family of satellites launched by the European Space Agency (ESA) under the Copernicus Programme.
If you use this dataset in your research, please cite it using the following format:
- class torchgeo.datasets.Sentinel2(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)¶
Bases:
Sentinel
Sentinel-2 dataset.
The Copernicus Sentinel-2 mission comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180° to each other. It aims at monitoring variability in land surface conditions, and its wide swath width (290 km) and high revisit time (10 days at the equator with one satellite, and 5 days with 2 satellites under cloud-free conditions which results in 2-3 days at mid-latitudes) will support monitoring of Earth’s surface changes.
- __init__(root='data', crs=None, res=None, bands=[], transforms=None, cache=True)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
bands (Sequence[str]) – bands to return (defaults to all bands)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
- Raises
FileNotFoundError – if no files are found in
root
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if the RGB bands are not included in
self.bands
- Return type
Changed in version 0.3: Method now takes a sample dict, not a Tensor. Additionally, possible to show subplot titles and/or use a custom suptitle.
Non-geospatial Datasets¶
NonGeoDataset
is designed for datasets that lack geospatial information. These datasets can still be combined using ConcatDataset
.
Dataset |
Task |
Source |
# Samples |
# Classes |
Size (px) |
Resolution (m) |
Bands |
---|---|---|---|---|---|---|---|
C |
Google Earth, Freesound |
5,075 |
13 |
512x512 |
0.5 |
RGB |
|
S |
Airbus Pléiades |
70 |
6 |
1,122x1,186 |
10 |
MSI |
|
C |
Sentinel-1/2 |
590,326 |
19–43 |
120x120 |
10 |
SAR, MSI |
|
C, R |
CSUAV AFRL, ISPRS, LINZ, AGRC |
388,435 |
2 |
256x256 |
0.15 |
RGB |
|
S |
Sentinel-2 |
4,688 |
7 |
3,035x2,016 |
10 |
MSI |
|
S |
DigitalGlobe +Vivid |
803 |
7 |
2,448x2,448 |
0.5 |
RGB |
|
S |
Aerial |
3,981 |
15 |
2,000x2,000 |
0.5 |
RGB |
|
S |
Sentinel-1 |
66,810 |
2 |
256x256 |
5–20 |
SAR |
|
C |
Sentinel-2 |
27,000 |
10 |
64x64 |
10 |
MSI |
|
OD |
Gaofen/Google Earth |
15,000 |
37 |
1,024x1,024 |
0.3–0.8 |
RGB |
|
OD |
Drone imagery |
1,543 |
4 |
1,500x1,500 |
RGB |
||
S |
Gaofen-2 |
150 |
15 |
6,800x7,200 |
3 |
RGB |
|
OD,C |
Aerial |
591 |
33 |
200x200 |
0.1–1 |
RGB |
|
S |
Aerial |
360 |
2 |
5,000x5,000 |
0.3 |
RGB |
|
S |
Aerial |
10,674 |
5 |
512x512 |
0.25–0.5 |
RGB |
|
CD |
Google Earth |
985 |
2 |
1,024x1,024 |
0.5 |
RGB |
|
S |
Google Earth |
5,987 |
7 |
1,024x1,024 |
0.3 |
RGB |
|
C |
Google Earth |
1M |
51–73 |
0.5–153 |
RGB |
||
OD |
PlanetScope |
707 |
1 |
256x256 |
3 |
RGB |
|
CD |
Sentinel-2 |
24 |
2 |
40–1,180 |
60 |
MSI |
|
C |
Google Earth |
30,400 |
38 |
256x256 |
0.06–5 |
RGB |
|
S |
Aerial |
38 |
6 |
6,000x6,000 |
0.05 |
MSI |
|
OD, R |
Aerial |
100 |
4,000x4,000 |
0.02 |
RGB |
||
C |
Google Earth |
31,500 |
45 |
256x256 |
0.2–30 |
RGB |
|
T |
Sentinel-2 |
100K–1M |
264x264 |
10 |
MSI |
||
S |
Sentinel-1/2, MODIS |
180,662 |
33 |
256x256 |
10 |
SAR, MSI |
|
C |
Sentinel-1/2 |
400,673 |
17 |
32x32 |
10 |
SAR, MSI |
|
I |
WorldView-2/3 Planet Lab Dove |
1,889–28,728 |
2 |
102–900 |
0.5–4 |
MSI |
|
R |
GOES 8–16 |
108,110 |
256x256 |
4K–8K |
MSI |
||
C |
USGS National Map |
21,000 |
21 |
256x256 |
0.3 |
RGB |
|
S |
NAIP Aerial |
100K |
4 |
RGB, NIR |
|||
S |
Aerial |
33 |
6 |
1,281–3,816 |
0.09 |
RGB |
|
I |
Google Earth, Vaihingen |
800 |
10 |
358–1,728 |
0.08–2 |
RGB |
|
CD |
Maxar |
3,732 |
4 |
1,024x1,024 |
0.8 |
RGB |
|
I, T |
Sentinel-2 |
116K |
48 |
24x24 |
10 |
MSI |
ADVANCE¶
- class torchgeo.datasets.ADVANCE(root='data', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
ADVANCE dataset.
The ADVANCE dataset is a dataset for audio visual scene recognition.
Dataset features:
5,075 pairs of geotagged audio recordings and images
three spectral bands - RGB (512x512 px)
10-second audio recordings
Dataset format:
images are three-channel jpgs
audio files are in wav format
Dataset classes:
airport
beach
bridge
farmland
forest
grassland
harbour
lake
orchard
residential
sparse shrub land
sports land
train station
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
scipy to load the audio files to tensors
- __init__(root='data', transforms=None, download=False, checksum=False)[source]¶
Initialize a new ADVANCE dataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
Benin Cashew Plantations¶
- class torchgeo.datasets.BeninSmallHolderCashews(root='data', chip_size=256, stride=128, bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'CLD'), transforms=None, download=False, api_key=None, checksum=False, verbose=False)¶
Bases:
NonGeoDataset
Smallholder Cashew Plantations in Benin dataset.
This dataset contains labels for cashew plantations in a 120 km2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final corrections based on Airbus Pléiades imagery. See this website for dataset details.
Specifically, the data consists of Sentinel 2 imagery from a 120 km2 area in the center of Benin over 71 points in time from 11/05/2019 to 10/30/2020 and polygon labels for 6 classes:
No data
Well-managed plantation
Poorly-managed planatation
Non-plantation
Residential
Background
Uncertain
If you use this dataset in your research, please cite the following:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
- __init__(root='data', chip_size=256, stride=128, bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'CLD'), transforms=None, download=False, api_key=None, checksum=False, verbose=False)[source]¶
Initialize a new Benin Smallholder Cashew Plantations Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
chip_size (int) – size of chips
stride (int) – spacing between chips, if less than chip_size, then there will be overlap between chips
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
verbose (bool) – if True, print messages when new tiles are loaded
- Raises
RuntimeError – if
download=False
but dataset is missing or checksum fails
- __len__()[source]¶
Return the number of chips in the dataset.
- Returns
length of the dataset
- Return type
- plot(sample, show_titles=True, time_step=0, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if the RGB bands are not included in
self.bands
- Return type
New in version 0.2.
BigEarthNet¶
- class torchgeo.datasets.BigEarthNet(root='data', split='train', bands='all', num_classes=19, transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
BigEarthNet dataset.
The BigEarthNet dataset is a dataset for multilabel remote sensing image scene classification.
Dataset features:
590,326 patches from 125 Sentinel-1 and Sentinel-2 tiles
Imagery from tiles in Europe between Jun 2017 - May 2018
12 spectral bands with 10-60 m per pixel resolution (base 120x120 px)
2 synthetic aperture radar bands (120x120 px)
43 or 19 scene classes from the 2018 CORINE Land Cover database (CLC 2018)
Dataset format:
images are composed of multiple single channel geotiffs
labels are multiclass, stored in a single json file per image
mapping of Sentinel-1 to Sentinel-2 patches are within Sentinel-1 json files
Sentinel-1 bands: (VV, VH)
Sentinel-2 bands: (B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
All bands: (VV, VH, B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
Sentinel-2 bands are of different spatial resolutions and upsampled to 10m
Dataset classes (43):
Agro-forestry areas
Airports
Annual crops associated with permanent crops
Bare rock
Beaches, dunes, sands
Broad-leaved forest
Burnt areas
Coastal lagoons
Complex cultivation patterns
Coniferous forest
Construction sites
Continuous urban fabric
Discontinuous urban fabric
Dump sites
Estuaries
Fruit trees and berry plantations
Green urban areas
Industrial or commercial units
Inland marshes
Intertidal flats
Land principally occupied by agriculture, with significant areas of natural vegetation
Mineral extraction sites
Mixed forest
Moors and heathland
Natural grassland
Non-irrigated arable land
Olive groves
Pastures
Peatbogs
Permanently irrigated land
Port areas
Rice fields
Road and rail networks and associated land
Salines
Salt marshes
Sclerophyllous vegetation
Sea and ocean
Sparsely vegetated areas
Sport and leisure facilities
Transitional woodland/shrub
Vineyards
Water bodies
Water courses
Dataset classes (19):
Urban fabric
Industrial or commercial units
Arable land
Permanent crops
Pastures
Complex cultivation patterns
Land principally occupied by agriculture, with significant areas of natural vegetation
Agro-forestry areas
Broad-leaved forest
Coniferous forest
Mixed forest
Natural grassland and sparsely vegetated areas
Moors, heathland and sclerophyllous vegetation
Transitional woodland, shrub
Beaches, dunes, sands
Inland wetlands
Coastal wetlands
Inland waters
Marine waters
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', split='train', bands='all', num_classes=19, transforms=None, download=False, checksum=False)[source]¶
Initialize a new BigEarthNet dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – train/val/test split to load
bands (str) – load Sentinel-1 bands, Sentinel-2, or both. one of {s1, s2, all}
num_classes (int) – number of classes to load in target. one of {19, 43}
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if
self.bands
is “s1”- Return type
New in version 0.2.
COWC¶
- class torchgeo.datasets.COWC(root='data', split='train', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
,ABC
Abstract base class for the COWC dataset.
The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. It is useful for training a device such as a deep neural network to learn to detect and/or count cars.
The dataset has the following attributes:
Data from overhead at 15 cm per pixel resolution at ground (all data is EO).
Data from six distinct locations: Toronto, Canada; Selwyn, New Zealand; Potsdam and Vaihingen, Germany; Columbus, Ohio and Utah, United States.
32,716 unique annotated cars. 58,247 unique negative examples.
Intentional selection of hard negative examples.
Established baseline for detection and counting tasks.
Extra testing scenes for use after validation.
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]¶
Initialize a new COWC dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
Kenya Crop Type¶
- class torchgeo.datasets.CV4AKenyaCropType(root='data', chip_size=256, stride=128, bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'CLD'), transforms=None, download=False, api_key=None, checksum=False, verbose=False)¶
Bases:
NonGeoDataset
CV4A Kenya Crop Type dataset.
Used in a competition in the Computer NonGeo for Agriculture (CV4A) workshop in ICLR 2020. See this website for dataset details.
Consists of 4 tiles of Sentinel 2 imagery from 13 different points in time.
Each tile has:
13 multi-band observations throughout the growing season. Each observation includes 12 bands from Sentinel-2 L2A product, and a cloud probability layer. The twelve bands are [B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12] (refer to Sentinel-2 documentation for more information about the bands). The cloud probability layer is a product of the Sentinel-2 atmospheric correction algorithm (Sen2Cor) and provides an estimated cloud probability (0-100%) per pixel. All of the bands are mapped to a common 10 m spatial resolution grid.
A raster layer indicating the crop ID for the fields in the training set.
A raster layer indicating field IDs for the fields (both training and test sets). Fields with a crop ID 0 are the test fields.
There are 3,286 fields in the train set and 1,402 fields in the test set.
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
- __init__(root='data', chip_size=256, stride=128, bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'CLD'), transforms=None, download=False, api_key=None, checksum=False, verbose=False)[source]¶
Initialize a new CV4A Kenya Crop Type Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
chip_size (int) – size of chips
stride (int) – spacing between chips, if less than chip_size, then there will be overlap between chips
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
verbose (bool) – if True, print messages when new tiles are loaded
- Raises
RuntimeError – if
download=False
but dataset is missing or checksum fails
- __len__()[source]¶
Return the number of chips in the dataset.
- Returns
length of the dataset
- Return type
Deep Globe Land Cover¶
- class torchgeo.datasets.DeepGlobeLandCover(root='data', split='train', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
DeepGlobe Land Cover Classification Challenge dataset.
The DeepGlobe Land Cover Classification Challenge dataset offers high-resolution sub-meter satellite imagery focusing for the task of semantic segmentation to detect areas of urban, agriculture, rangeland, forest, water, barren, and unknown. It contains 1,146 satellite images of size 2448 x 2448 pixels in total, split into training/validation/test sets, the original dataset can be downloaded from Kaggle. However, we only use the training dataset with 803 images since the original test and valid dataset are not accompanied by labels. The dataset that we use with a custom train/test split can be downloaded from Kaggle (created as a part of Computer Vision by Deep Learning (CS4245) course offered at TU Delft).
Dataset format:
images are RGB data
masks are RGB image with with unique RGB values representing the class
Dataset classes:
Urban land
Agriculture land
Rangeland
Forest land
Water
Barren land
Unknown
File names for satellite images and the corresponding mask image are id_sat.jpg and id_mask.png, where id is an integer assigned to every image.
If you use this dataset in your research, please cite the following paper:
New in version 0.3.
- __init__(root='data', split='train', transforms=None, checksum=False)[source]¶
Initialize a new DeepGlobeLandCover dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
DFC2022¶
- class torchgeo.datasets.DFC2022(root='data', split='train', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
DFC2022 dataset.
The DFC2022 dataset is used as a benchmark dataset for the 2022 IEEE GRSS Data Fusion Contest and extends the MiniFrance dataset for semi-supervised semantic segmentation. The dataset consists of a train set containing labeled and unlabeled imagery and an unlabeled validation set. The dataset can be downloaded from the IEEEDataPort DFC2022 website.
Dataset features:
RGB aerial images at 0.5 m per pixel spatial resolution (~2,000x2,0000 px)
DEMs at 1 m per pixel spatial resolution (~1,000x1,0000 px)
Masks at 0.5 m per pixel spatial resolution (~2,000x2,0000 px)
16 land use/land cover categories
Images collected from the IGN BD ORTHO database
DEMs collected from the IGN RGE ALTI database
Labels collected from the UrbanAtlas 2012 database
Data collected from 19 regions in France
Dataset format:
images are three-channel geotiffs
DEMS are single-channel geotiffs
masks are single-channel geotiffs with the pixel values represent the class
Dataset classes:
No information
Urban fabric
Industrial, commercial, public, military, private and transport units
Mine, dump and construction sites
Artificial non-agricultural vegetated areas
Arable land (annual crops)
Permanent crops
Pastures
Complex and mixed cultivation patterns
Orchards at the fringe of urban classes
Forests
Herbaceous vegetation associations
Open spaces with little or no vegetation
Wetlands
Water
Clouds and Shadows
If you use this dataset in your research, please cite the following paper:
New in version 0.3.
- __init__(root='data', split='train', transforms=None, checksum=False)[source]¶
Initialize a new DFC2022 dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
is invalid
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
ETCI2021 Flood Detection¶
- class torchgeo.datasets.ETCI2021(root='data', split='train', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
ETCI 2021 Flood Detection dataset.
The ETCI2021 dataset is a dataset for flood detection
Dataset features:
33,405 VV & VH Sentinel-1 Synthetic Aperture Radar (SAR) images
2 binary masks per image representing water body & flood, respectively
2 polarization band images (VV, VH) of 3 RGB channels per band
3 RGB channels per band generated by the Hybrid Pluggable Processing Pipeline (hyp3)
Images with 5x20m per pixel resolution (256x256) px) taken in Interferometric Wide Swath acquisition mode
Flood events from 5 different regions
Dataset format:
VV band three-channel png
VH band three-channel png
water body mask single-channel png where no water body = 0, water body = 255
flood mask single-channel png where no flood = 0, flood = 255
Dataset classes:
no flood/water
flood/water
If you use this dataset in your research, please add the following to your acknowledgements section:
The authors would like to thank the NASA Earth Science Data Systems Program, NASA Digital Transformation AI/ML thrust, and IEEE GRSS for organizing the ETCI competition.
- __init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]¶
Initialize a new ETCI 2021 dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “val”, or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
EuroSAT¶
- class torchgeo.datasets.EuroSAT(root='data', split='train', bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B08A', 'B09', 'B10', 'B11', 'B12'), transforms=None, download=False, checksum=False)¶
Bases:
NonGeoClassificationDataset
EuroSAT dataset.
The EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consists of 10 target classes with a total of 27,000 labeled and geo-referenced images.
Dataset format:
rasters are 13-channel GeoTiffs
labels are values in the range [0,9]
Dataset classes:
Industrial Buildings
Residential Buildings
Annual Crop
Permanent Crop
River
Sea and Lake
Herbaceous Vegetation
Highway
Pasture
Forest
This dataset uses the train/val/test splits defined in the “In-domain representation learning for remote sensing” paper:
If you use this dataset in your research, please cite the following papers:
- __init__(root='data', split='train', bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B08A', 'B09', 'B10', 'B11', 'B12'), transforms=None, download=False, checksum=False)[source]¶
Initialize a new EuroSAT dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “val”, or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
New in version 0.3: The bands parameter.
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if RGB bands are not found in dataset
- Return type
New in version 0.2.
FAIR1M¶
- class torchgeo.datasets.FAIR1M(root='data', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
FAIR1M dataset.
The FAIR1M dataset is a dataset for remote sensing fine-grained oriented object detection.
Dataset features:
15,000+ images with 0.3-0.8 m per pixel resolution (1,000-10,000 px)
1 million object instances
5 object categories, 37 object sub-categories
three spectral bands - RGB
images taken by Gaofen satellites and Google Earth
Dataset format:
images are three-channel tiffs
labels are xml files with PASCAL VOC like annotations
Dataset classes:
Passenger Ship
Motorboat
Fishing Boat
Tugboat
other-ship
Engineering Ship
Liquid Cargo Ship
Dry Cargo Ship
Warship
Small Car
Bus
Cargo Truck
Dump Truck
other-vehicle
Van
Trailer
Tractor
Excavator
Truck Tractor
Boeing737
Boeing747
Boeing777
Boeing787
ARJ21
C919
A220
A321
A330
A350
other-airplane
Baseball Field
Basketball Court
Football Field
Tennis Court
Roundabout
Intersection
Bridge
If you use this dataset in your research, please cite the following paper:
New in version 0.2.
- __init__(root='data', transforms=None, checksum=False)[source]¶
Initialize a new FAIR1M dataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
Forest Damage¶
- class torchgeo.datasets.ForestDamage(root='data', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
Forest Damage dataset.
The ForestDamage dataset contains drone imagery that can be used for tree identification, as well as tree damage classification for larch trees.
Dataset features:
1543 images
101,878 tree annotations
subset of 840 images contain 44,522 annotations about tree health (Healthy (H), Light Damage (LD), High Damage (HD)), all other images have “other” as damage level
Dataset format:
images are three-channel jpgs
annotations are in Pascal VOC XML format
Dataset Classes:
other
healthy
light damage
high damage
If the download fails or stalls, it is recommended to try azcopy as suggested here. It is expected that the downloaded data file with name
Data_Set_Larch_Casebearer
can be found inroot
.If you use this dataset in your research, please use the following citation:
Swedish Forest Agency (2021): Forest Damages - Larch Casebearer 1.0. National Forest Data Lab. Dataset.
New in version 0.3.
- __init__(root='data', transforms=None, download=False, checksum=False)[source]¶
Initialize a new ForestDamage dataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
GID-15¶
- class torchgeo.datasets.GID15(root='data', split='train', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
GID-15 dataset.
The GID-15 dataset is a dataset for semantic segmentation.
Dataset features:
images taken by the Gaofen-2 (GF-2) satellite over 60 cities in China
masks representing 15 semantic categories
three spectral bands - RGB
150 with 3 m per pixel resolution (6800x7200 px)
Dataset format:
images are three-channel pngs
masks are single-channel pngs
colormapped masks are 3 channel tifs
Dataset classes:
background
industrial_land
urban_residential
rural_residential
traffic_land
paddy_field
irrigated_land
dry_cropland
garden_plot
arbor_woodland
shrub_land
natural_grassland
artificial_grassland
river
lake
pond
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]¶
Initialize a new GID-15 dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “val”, or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
IDTReeS¶
- class torchgeo.datasets.IDTReeS(root='data', split='train', task='task1', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
IDTReeS dataset.
The IDTReeS dataset is a dataset for tree crown detection.
Dataset features:
RGB Image, Canopy Height Model (CHM), Hyperspectral Image (HSI), LiDAR Point Cloud
Remote sensing and field data generated by the National Ecological Observatory Network (NEON)
0.1 - 1m resolution imagery
Task 1 - object detection (tree crown delination)
Task 2 - object classification (species classification)
Train set contains 85 images
Test set (task 1) contains 153 images
Test set (task 2) contains 353 images and tree crown polygons
Dataset format:
optical - three-channel RGB 200x200 geotiff
canopy height model - one-channel 20x20 geotiff
hyperspectral - 369-channel 20x20 geotiff
point cloud - Nx3 LAS file (.las), some files contain RGB colors per point
shapely files (.shp) containing polygons
csv file containing species labels and other metadata for each polygon
Dataset classes:
ACPE
ACRU
ACSA3
AMLA
BETUL
CAGL8
CATO6
FAGR
GOLA
LITU
LYLU3
MAGNO
NYBI
NYSY
OXYDE
PEPA37
PIEL
PIPA2
PINUS
PITA
PRSE2
QUAL
QUCO2
QUGE2
QUHE2
QULA2
QULA3
QUMO4
QUNI
QURU
QUERC
ROPS
TSCA
If you use this dataset in your research, please cite the following paper:
New in version 0.2.
- __init__(root='data', split='train', task='task1', transforms=None, download=False, checksum=False)[source]¶
Initialize a new IDTReeS dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
task (str) – ‘task1’ for detection, ‘task2’ for detection + classification (only relevant for split=’test’)
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
ImportError – if laspy or pandas are are not installed
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
- plot(sample, show_titles=True, suptitle=None, hsi_indices=(0, 1, 2))[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Return type
- plot_las(index, colormap=None)[source]¶
Plot a sample point cloud at the index.
- Parameters
- Returns
- a open3d.visualizer.Visualizer object. Use
Visualizer.run() to display
- Raises
ImportError – if open3d is not installed
- Return type
Inria Aerial Image Labeling¶
- class torchgeo.datasets.InriaAerialImageLabeling(root, split='train', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
Inria Aerial Image Labeling Dataset.
The Inria Aerial Image Labeling dataset is a building detection dataset over dissimilar settlements ranging from densely populated areas to alpine towns. Refer to the dataset homepage to download the dataset.
Dataset features:
Coverage of 810 km2 (405 km2 for training and 405 km2 for testing)
Aerial orthorectified color imagery with a spatial resolution of 0.3 m
Number of images: 360 (train: 180, test: 180)
Train cities: Austin, Chicago, Kitsap, West Tyrol, Vienna
Test cities: Bellingham, Bloomington, Innsbruck, San Francisco, East Tyrol
Dataset format:
Imagery - RGB aerial GeoTIFFs of shape 5000 x 5000
Labels - RGB aerial GeoTIFFs of shape 5000 x 5000
If you use this dataset in your research, please cite the following paper:
New in version 0.3.
- __init__(root, split='train', transforms=None, checksum=False)[source]¶
Initialize a new InriaAerialImageLabeling Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – train/test split
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version.
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
is invalidRuntimeError – if dataset is missing
- __len__()[source]¶
Return the number of samples in the dataset.
- Returns
length of the dataset
- Return type
LandCover.ai¶
- class torchgeo.datasets.LandCoverAI(root='data', split='train', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
LandCover.ai dataset.
The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images. This implementation is specifically for Version 1 of Landcover.ai.
Dataset features:
land cover from Poland, Central Europe
three spectral bands - RGB
33 orthophotos with 25 cm per pixel resolution (~9000x9500 px)
8 orthophotos with 50 cm per pixel resolution (~4200x4700 px)
total area of 216.27 km2
Dataset format:
rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system
masks are single-channel GeoTiffs with EPSG:2180 spatial reference system
Dataset classes:
building (1.85 km2)
woodland (72.02 km2)
water (13.15 km2)
road (3.5 km2)
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
opencv-python to generate the train/val/test split
- __init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]¶
Initialize a new LandCover.ai dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “val”, or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
LEVIR-CD+¶
- class torchgeo.datasets.LEVIRCDPlus(root='data', split='train', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
LEVIR-CD+ dataset.
The LEVIR-CD+ dataset is a dataset for building change detection.
Dataset features:
image pairs of 20 different urban regions across Texas between 2002-2020
binary change masks representing building change
three spectral bands - RGB
985 image pairs with 50 cm per pixel resolution (~1024x1024 px)
Dataset format:
images are three-channel pngs
masks are single-channel pngs where no change = 0, change = 255
Dataset classes:
no change
change
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]¶
Initialize a new LEVIR-CD+ dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
LoveDA¶
- class torchgeo.datasets.LoveDA(root='data', split='train', scene=['urban', 'rural'], transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
LoveDA dataset.
The LoveDA datataset is a semantic segmentation dataset.
Dataset features:
2713 urban scene and 3274 rural scene HSR images, spatial resolution of 0.3m
image source is Google Earth platform
total of 166768 annotated objects from Nanjing, Changzhou and Wuhan cities
dataset comes with predefined train, validation, and test set
dataset differentiates between ‘rural’ and ‘urban’ images
Dataset format:
images are three-channel pngs with dimension 1024x1024
segmentation masks are single-channel pngs
Dataset classes:
background
building
road
water
barren
forest
agriculture
No-data regions assigned with 0 and should be ignored.
If you use this dataset in your research, please cite the following paper:
New in version 0.2.
- __init__(root='data', split='train', scene=['urban', 'rural'], transforms=None, download=False, checksum=False)[source]¶
Initialize a new LoveDA dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “val”, or “test”
scene (List[str]) – specify whether to load only ‘urban’, only ‘rural’ or both
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidAssertionError – if
scene
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
Million-AID¶
- class torchgeo.datasets.MillionAID(root='data', task='multi-class', split='train', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
Million-AID Dataset.
The MillionAID dataset consists of one million aerial images from Google Earth Engine that offers either a multi-class learning task with 51 classes or a multi-label learning task with 73 different possible labels. For more details please consult the accompanying paper.
Dataset features:
RGB aerial images with varying resolutions from 0.5 m to 153 m per pixel
images within classes can have different pixel dimension
Dataset format:
images are three-channel jpg
If you use this dataset in your research, please cite the following paper:
New in version 0.3.
- __init__(root='data', task='multi-class', split='train', transforms=None, checksum=False)[source]¶
Initialize a new MillionAID dataset instance.
- Parameters
root (str) – root directory where dataset can be found
task (str) – type of task, either “multi-class” or “multi-label”
split (str) – train or test split
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if dataset is not found
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
NASA Marine Debris¶
- class torchgeo.datasets.NASAMarineDebris(root='data', transforms=None, download=False, api_key=None, checksum=False, verbose=False)¶
Bases:
NonGeoDataset
NASA Marine Debris dataset.
The NASA Marine Debris dataset is a dataset for detection of floating marine debris in satellite imagery.
Dataset features:
707 patches with 3 m per pixel resolution (256x256 px)
three spectral bands - RGB
1 object class: marine_debris
images taken by Planet Labs PlanetScope satellites
imagery taken from 2016-2019 from coasts of Greece, Honduras, and Ghana
Dataset format:
images are three-channel geotiffs in uint8 format
labels are numpy files (.npy) containing bounding box (xyxy) coordinates
additional: images in jpg format and labels in geojson format
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
New in version 0.2.
- __init__(root='data', transforms=None, download=False, api_key=None, checksum=False, verbose=False)[source]¶
Initialize a new NASA Marine Debris Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
verbose (bool) – if True, print messages when new tiles are loaded
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
OSCD¶
- class torchgeo.datasets.OSCD(root='data', split='train', bands='all', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
OSCD dataset.
The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates. Imagery comes from Sentinel-2 which contains varying resolutions per band.
Dataset format:
images are 13-channel tifs
masks are single-channel pngs where no change = 0, change = 255
Dataset classes:
no change
change
If you use this dataset in your research, please cite the following paper:
New in version 0.2.
- __init__(root='data', split='train', bands='all', transforms=None, download=False, checksum=False)[source]¶
Initialize a new OSCD dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
PatternNet¶
- class torchgeo.datasets.PatternNet(root='data', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoClassificationDataset
PatternNet dataset.
The PatternNet dataset is a dataset for remote sensing scene classification and image retrieval.
Dataset features:
30,400 images with 6-50 cm per pixel resolution (256x256 px)
three spectral bands - RGB
38 scene classes, 800 images per class
Dataset format:
images are three-channel jpgs
Dataset classes:
airplane
baseball_field
basketball_court
beach
bridge
cemetery
chaparral
christmas_tree_farm
closed_road
coastal_mansion
crosswalk
dense_residential
ferry_terminal
football_field
forest
freeway
golf_course
harbor
intersection
mobile_home_park
nursing_home
oil_gas_field
oil_well
overpass
parking_lot
parking_space
railway
river
runway
runway_marking
shipping_yard
solar_panel
sparse_residential
storage_tank
swimming_pool
tennis_court
transformer_station
wastewater_treatment_plant
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', transforms=None, download=False, checksum=False)[source]¶
Initialize a new PatternNet dataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
Potsdam¶
- class torchgeo.datasets.Potsdam2D(root='data', split='train', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
Potsdam 2D Semantic Segmentation dataset.
The Potsdam dataset is a dataset for urban semantic segmentation used in the 2D Semantic Labeling Contest - Potsdam. This dataset uses the “4_Ortho_RGBIR.zip” and “5_Labels_all.zip” files to create the train/test sets used in the challenge. The dataset can be requested at the challenge homepage. Note, the server contains additional data for 3D Semantic Labeling which are currently not supported.
Dataset format:
images are 4-channel geotiffs
masks are 3-channel geotiffs with unique RGB values representing the class
Dataset classes:
Clutter/background
Impervious surfaces
Building
Low Vegetation
Tree
Car
If you use this dataset in your research, please cite the following paper:
New in version 0.2.
- __init__(root='data', split='train', transforms=None, checksum=False)[source]¶
Initialize a new Potsdam dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
ReforesTree¶
- class torchgeo.datasets.ReforesTree(root='data', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
ReforesTree dataset.
The ReforesTree dataset contains drone imagery that can be used for tree crown detection, tree species classification and Aboveground Biomass (AGB) estimation.
Dataset features:
100 high resolution RGB drone images at 2 cm/pixel of size 4,000 x 4,000 px
more than 4,600 tree crown box annotations
tree crown matched with field measurements of diameter at breast height (DBH), and computed AGB and carbon values
Dataset format:
images are three-channel pngs
annotations are csv file
Dataset Classes:
other
banana
cacao
citrus
fruit
timber
If you use this dataset in your research, please cite the following paper:
New in version 0.3.
- __init__(root='data', transforms=None, download=False, checksum=False)[source]¶
Initialize a new ReforesTree dataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
RESISC45¶
- class torchgeo.datasets.RESISC45(root='data', split='train', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoClassificationDataset
RESISC45 dataset.
The RESISC45 dataset is a dataset for remote sensing image scene classification.
Dataset features:
31,500 images with 0.2-30 m per pixel resolution (256x256 px)
three spectral bands - RGB
45 scene classes, 700 images per class
images extracted from Google Earth from over 100 countries
images conditions with high variability (resolution, weather, illumination)
Dataset format:
images are three-channel jpgs
Dataset classes:
airplane
airport
baseball_diamond
basketball_court
beach
bridge
chaparral
church
circular_farmland
cloud
commercial_area
dense_residential
desert
forest
freeway
golf_course
ground_track_field
harbor
industrial_area
intersection
island
lake
meadow
medium_residential
mobile_home_park
mountain
overpass
palace
parking_lot
railway
railway_station
rectangular_farmland
river
roundabout
runway
sea_ice
ship
snowberg
sparse_residential
stadium
storage_tank
tennis_court
terrace
thermal_power_station
wetland
This dataset uses the train/val/test splits defined in the “In-domain representation learning for remote sensing” paper:
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]¶
Initialize a new RESISC45 dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “val”, or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
Seasonal Contrast¶
- class torchgeo.datasets.SeasonalContrastS2(root='data', version='100k', bands=['B4', 'B3', 'B2'], transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
Sentinel 2 imagery from the Seasonal Contrast paper.
The Seasonal Contrast imagery dataset contains Sentinel 2 imagery patches sampled from different points in time around the 10k most populated cities on Earth.
Dataset features:
Two versions: 100K and 1M patches
12 band Sentinel 2 imagery from 5 points in time at each location
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', version='100k', bands=['B4', 'B3', 'B2'], transforms=None, download=False, checksum=False)[source]¶
Initialize a new SeCo dataset instance.
- Parameters
root (str) – root directory where dataset can be found
version (str) – one of “100k” or “1m” for the version of the dataset to use
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
version
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if the RGB bands are included in
self.bands
or the sample contains a “prediction” key- Return type
New in version 0.2.
SEN12MS¶
- class torchgeo.datasets.SEN12MS(root='data', split='train', bands=('VV', 'VH', 'B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12'), transforms=None, checksum=False)¶
Bases:
NonGeoDataset
SEN12MS dataset.
The SEN12MS dataset contains 180,662 patch triplets of corresponding Sentinel-1 dual-pol SAR data, Sentinel-2 multi-spectral images, and MODIS-derived land cover maps. The patches are distributed across the land masses of the Earth and spread over all four meteorological seasons. This is reflected by the dataset structure. All patches are provided in the form of 16-bit GeoTiffs containing the following specific information:
Sentinel-1 SAR: 2 channels corresponding to sigma nought backscatter values in dB scale for VV and VH polarization.
Sentinel-2 Multi-Spectral: 13 channels corresponding to the 13 spectral bands (B1, B2, B3, B4, B5, B6, B7, B8, B8a, B9, B10, B11, B12).
MODIS Land Cover: 4 channels corresponding to IGBP, LCCS Land Cover, LCCS Land Use, and LCCS Surface Hydrology layers.
If you use this dataset in your research, please cite the following paper:
Note
This dataset can be automatically downloaded using the following bash script:
for season in 1158_spring 1868_summer 1970_fall 2017_winter do for source in lc s1 s2 do wget "ftp://m1474000:m1474000@dataserv.ub.tum.de/ROIs${season}_${source}.tar.gz" tar xvzf "ROIs${season}_${source}.tar.gz" done done for split in train test do wget "https://raw.githubusercontent.com/schmitt-muc/SEN12MS/master/splits/${split}_list.txt" done
or manually downloaded from https://dataserv.ub.tum.de/s/m1474000 and https://github.com/schmitt-muc/SEN12MS/tree/master/splits. This download will likely take several hours.
- __init__(root='data', split='train', bands=('VV', 'VH', 'B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12'), transforms=None, checksum=False)[source]¶
Initialize a new SEN12MS dataset instance.
The
bands
argument allows for the subsetting of bands returned by the dataset. Integers inbands
index into a stack of Sentinel 1 and Sentinel 2 imagery. Indices 0 and 1 correspond to the Sentinel 1 imagery where indices 2 through 14 correspond to the Sentinel 2 imagery.- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
bands (Sequence[str]) – a sequence of band indices to use where the indices correspond to the array index of combined Sentinel 1 and Sentinel 2
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if data is not found in
root
, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
So2Sat¶
- class torchgeo.datasets.So2Sat(root='data', split='train', bands=('S1B1', 'S1B2', 'S1B3', 'S1B4', 'S1B5', 'S1B6', 'S1B7', 'S1B8', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B08A', 'B11 SWIR', 'B12 SWIR'), transforms=None, checksum=False)¶
Bases:
NonGeoDataset
So2Sat dataset.
The So2Sat dataset consists of corresponding synthetic aperture radar and multispectral optical image data acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and a corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world, and comes with a split into fully independent, non-overlapping training, validation, and test sets.
This implementation focuses on the 2nd version of the dataset as described in the author’s github repository https://github.com/zhu-xlab/So2Sat-LCZ42 and hosted at https://mediatum.ub.tum.de/1483140. This version is identical to the first version of the dataset but includes the test data. The splits are defined as follows:
Training: 42 cities around the world
Validation: western half of 10 other cities covering 10 cultural zones
Testing: eastern half of the 10 other cities
Dataset classes:
Compact high rise
Compact middle rise
Compact low rise
Open high rise
Open mid rise
Open low rise
Lightweight low rise
Large low rise
Sparsely built
Heavy industry
Dense trees
Scattered trees
Bush, scrub
Low plants
Bare rock or paved
Bare soil or sand
Water
If you use this dataset in your research, please cite the following paper:
Note
This dataset can be automatically downloaded using the following bash script:
for split in training validation testing do wget ftp://m1483140:m1483140@dataserv.ub.tum.de/$split.h5 done
or manually downloaded from https://dataserv.ub.tum.de/index.php/s/m1483140 This download will likely take several hours.
- __init__(root='data', split='train', bands=('S1B1', 'S1B2', 'S1B3', 'S1B4', 'S1B5', 'S1B6', 'S1B7', 'S1B8', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B08A', 'B11 SWIR', 'B12 SWIR'), transforms=None, checksum=False)[source]¶
Initialize a new So2Sat dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “validation”, or “test”
bands (Sequence[str]) – a sequence of band names to use where the indices correspond to the array index of combined Sentinel 1 and Sentinel 2
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if data is not found in
root
, or checksums don’t match
New in version 0.3: The bands parameter.
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
- plot(sample, show_titles=True, suptitle=None)[source]¶
Plot a sample from the dataset.
- Parameters
- Returns
a matplotlib Figure with the rendered sample
- Raises
ValueError – if RGB bands are not found in dataset
- Return type
New in version 0.2.
SpaceNet¶
- class torchgeo.datasets.SpaceNet(root, image, collections=[], transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
NonGeoDataset
,ABC
Abstract base class for the SpaceNet datasets.
The SpaceNet datasets are a set of datasets that all together contain >11M building footprints and ~20,000 km of road labels mapped over high-resolution satellite imagery obtained from a variety of sensors such as Worldview-2, Worldview-3 and Dove.
- __init__(root, image, collections=[], transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new SpaceNet Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
image (str) – image selection
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version.
download (bool) – if True, download dataset and store it in the root directory.
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
but dataset is missing
- __len__()[source]¶
Return the number of samples in the dataset.
- Returns
length of the dataset
- Return type
- class torchgeo.datasets.SpaceNet1(root, image='rgb', transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
SpaceNet
SpaceNet 1: Building Detection v1 Dataset.
SpaceNet 1 is a dataset of building footprints over the city of Rio de Janeiro.
Dataset features:
No. of images: 6940 (8 Band) + 6940 (RGB)
No. of polygons: 382,534 building labels
Area Coverage: 2544 sq km
GSD: 1 m (8 band), 50 cm (rgb)
Chip size: 101 x 110 (8 band), 406 x 438 (rgb)
Dataset format:
Imagery - Worldview-2 GeoTIFFs
8Band.tif (Multispectral)
RGB.tif (Pansharpened RGB)
Labels - GeoJSON
labels.geojson
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
- __init__(root, image='rgb', transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new SpaceNet 1 Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
image (str) – image selection which must be “rgb” or “8band”
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version.
download (bool) – if True, download dataset and store it in the root directory.
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
but dataset is missing
- class torchgeo.datasets.SpaceNet2(root, image='PS-RGB', collections=[], transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
SpaceNet
SpaceNet 2: Building Detection v2 Dataset.
SpaceNet 2 is a dataset of building footprints over the cities of Las Vegas, Paris, Shanghai and Khartoum.
Collection features:
AOI
Area (km2)
# Images
# Buildings
Las Vegas
216
3850
151,367
Paris
1030
1148
23,816
Shanghai
1000
4582
92,015
Khartoum
765
1012
35,503
Imagery features:
PAN
MS
PS-MS
PS-RGB
GSD (m)
0.31
1.24
0.30
0.30
Chip size (px)
650 x 650
162 x 162
650 x 650
650 x 650
Dataset format:
Imagery - Worldview-3 GeoTIFFs
PAN.tif (Panchromatic)
MS.tif (Multispectral)
PS-MS (Pansharpened Multispectral)
PS-RGB (Pansharpened RGB)
Labels - GeoJSON
label.geojson
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
- __init__(root, image='PS-RGB', collections=[], transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new SpaceNet 2 Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
image (str) – image selection which must be in [“MS”, “PAN”, “PS-MS”, “PS-RGB”]
collections (List[str]) – collection selection which must be a subset of: [sn2_AOI_2_Vegas, sn2_AOI_3_Paris, sn2_AOI_4_Shanghai, sn2_AOI_5_Khartoum]. If unspecified, all collections will be used.
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory.
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
but dataset is missing
- class torchgeo.datasets.SpaceNet3(root, image='PS-RGB', speed_mask=False, collections=[], transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
SpaceNet
SpaceNet 3: Road Network Detection.
SpaceNet 3 is a dataset of road networks over the cities of Las Vegas, Paris, Shanghai, and Khartoum.
Collection features:
AOI
Area (km2)
# Images
# Road Network Labels (km)
Vegas
216
854
3685
Paris
1030
257
425
Shanghai
1000
1028
3537
Khartoum
765
283
1030
Imagery features:
PAN
MS
PS-MS
PS-RGB
GSD (m)
0.31
1.24
0.30
0.30
Chip size (px)
1300 x 1300
325 x 325
1300 x 1300
1300 x 1300
Dataset format:
Imagery - Worldview-3 GeoTIFFs
PAN.tif (Panchromatic)
MS.tif (Multispectral)
PS-MS (Pansharpened Multispectral)
PS-RGB (Pansharpened RGB)
Labels - GeoJSON
labels.geojson
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
New in version 0.3.
- __init__(root, image='PS-RGB', speed_mask=False, collections=[], transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new SpaceNet 3 Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
image (str) – image selection which must be in [“MS”, “PAN”, “PS-MS”, “PS-RGB”]
speed_mask (Optional[bool]) – use multi-class speed mask (created by binning roads at 10 mph increments) as label if true, else use binary mask
collections (List[str]) – collection selection which must be a subset of: [sn3_AOI_2_Vegas, sn3_AOI_3_Paris, sn3_AOI_4_Shanghai, sn3_AOI_5_Khartoum]. If unspecified, all collections will be used.
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory.
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
but dataset is missing
- class torchgeo.datasets.SpaceNet4(root, image='PS-RGBNIR', angles=[], transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
SpaceNet
SpaceNet 4: Off-Nadir Buildings Dataset.
SpaceNet 4 is a dataset of 27 WV-2 imagery captured at varying off-nadir angles and associated building footprints over the city of Atlanta. The off-nadir angle ranges from 7 degrees to 54 degrees.
Dataset features:
No. of chipped images: 28,728 (PAN/MS/PS-RGBNIR)
No. of label files: 1064
No. of building footprints: >120,000
Area Coverage: 665 sq km
Chip size: 225 x 225 (MS), 900 x 900 (PAN/PS-RGBNIR)
Dataset format:
Imagery - Worldview-2 GeoTIFFs
PAN.tif (Panchromatic)
MS.tif (Multispectral)
PS-RGBNIR (Pansharpened RGBNIR)
Labels - GeoJSON
labels.geojson
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
- __init__(root, image='PS-RGBNIR', angles=[], transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new SpaceNet 4 Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
image (str) – image selection which must be in [“MS”, “PAN”, “PS-RGBNIR”]
angles (List[str]) – angle selection which must be in [“nadir”, “off-nadir”, “very-off-nadir”]
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory.
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
but dataset is missing
- class torchgeo.datasets.SpaceNet5(root, image='PS-RGB', speed_mask=False, collections=[], transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
SpaceNet3
SpaceNet 5: Automated Road Network Extraction and Route Travel Time Estimation.
SpaceNet 5 is a dataset of road networks over the cities of Moscow, Mumbai and San Juan (unavailable).
Collection features:
AOI
Area (km2)
# Images
# Road Network Labels (km)
Moscow
1353
1353
3066
Mumbai
1021
1016
1951
Imagery features:
PAN
MS
PS-MS
PS-RGB
GSD (m)
0.31
1.24
0.30
0.30
Chip size (px)
1300 x 1300
325 x 325
1300 x 1300
1300 x 1300
Dataset format:
Imagery - Worldview-3 GeoTIFFs
PAN.tif (Panchromatic)
MS.tif (Multispectral)
PS-MS (Pansharpened Multispectral)
PS-RGB (Pansharpened RGB)
Labels - GeoJSON
labels.geojson
If you use this dataset in your research, please use the following citation:
The SpaceNet Partners, “SpaceNet5: Automated Road Network Extraction and Route Travel Time Estimation from Satellite Imagery”, https://spacenet.ai/sn5-challenge/
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
New in version 0.2.
- __init__(root, image='PS-RGB', speed_mask=False, collections=[], transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new SpaceNet 5 Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
image (str) – image selection which must be in [“MS”, “PAN”, “PS-MS”, “PS-RGB”]
speed_mask (Optional[bool]) – use multi-class speed mask (created by binning roads at 10 mph increments) as label if true, else use binary mask
collections (List[str]) – collection selection which must be a subset of: [sn5_AOI_7_Moscow, sn5_AOI_8_Mumbai]. If unspecified, all collections will be used.
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory.
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
but dataset is missing
- class torchgeo.datasets.SpaceNet7(root, split='train', transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
SpaceNet
SpaceNet 7: Multi-Temporal Urban Development Challenge.
SpaceNet 7 is a dataset which consist of medium resolution (4.0m) satellite imagery mosaics acquired from Planet Labs’ Dove constellation between 2017 and 2020. It includes ≈ 24 images (one per month) covering > 100 unique geographies, and comprises > 40,000 km2 of imagery and exhaustive polygon labels of building footprints therein, totaling over 11M individual annotations.
Dataset features:
No. of train samples: 1423
No. of test samples: 466
No. of building footprints: 11,080,000
Area Coverage: 41,000 sq km
Chip size: 1023 x 1023
GSD: ~4m
Dataset format:
Imagery - Planet Dove GeoTIFF
mosaic.tif
Labels - GeoJSON
labels.geojson
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
New in version 0.2.
- __init__(root, split='train', transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new SpaceNet 7 Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – split selection which must be in [“train”, “test”]
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory.
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
but dataset is missing
Tropical Cyclone¶
- class torchgeo.datasets.TropicalCycloneWindEstimation(root='data', split='train', transforms=None, download=False, api_key=None, checksum=False)¶
Bases:
NonGeoDataset
Tropical Cyclone Wind Estimation Competition dataset.
A collection of tropical storms in the Atlantic and East Pacific Oceans from 2000 to 2019 with corresponding maximum sustained surface wind speed. This dataset is split into training and test categories for the purpose of a competition.
See https://www.drivendata.org/competitions/72/predict-wind-speeds/ for more information about the competition.
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
radiant-mlhub to download the imagery and labels from the Radiant Earth MLHub
- __init__(root='data', split='train', transforms=None, download=False, api_key=None, checksum=False)[source]¶
Initialize a new Tropical Cyclone Wind Estimation Competition Dataset.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
api_key (Optional[str]) – a RadiantEarth MLHub API key to use for downloading the dataset
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
but dataset is missing or checksum fails
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
UC Merced¶
- class torchgeo.datasets.UCMerced(root='data', split='train', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoClassificationDataset
UC Merced dataset.
The UC Merced dataset is a land use classification dataset of 2.1k 256x256 1ft resolution RGB images of urban locations around the U.S. extracted from the USGS National Map Urban Area Imagery collection with 21 land use classes (100 images per class).
Dataset features:
land use class labels from around the U.S.
three spectral bands - RGB
21 classes
Dataset classes:
agricultural
airplane
baseballdiamond
beach
buildings
chaparral
denseresidential
forest
freeway
golfcourse
harbor
intersection
mediumresidential
mobilehomepark
overpass
parkinglot
river
runway
sparseresidential
storagetanks
tenniscourt
This dataset uses the train/val/test splits defined in the “In-domain representation learning for remote sensing” paper:
If you use this dataset in your research, please cite the following paper:
- __init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]¶
Initialize a new UC Merced dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train”, “val”, or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
and data is not found, or checksums don’t match
USAVars¶
- class torchgeo.datasets.USAVars(root='data', split='train', labels=['housing', 'income', 'roads', 'nightlights', 'population', 'elevation', 'treecover'], transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
USAVars dataset.
The USAVars dataset is reproduction of the dataset used in the paper “A generalizable and accessible approach to machine learning with global satellite imagery”. Specifically, this dataset includes 1 sq km. crops of NAIP imagery resampled to 4m/px cenetered on ~100k points that are sampled randomly from the contiguous states in the USA. Each point contains three continous valued labels (taken from the dataset released in the paper): tree cover percentage, elevation, and population density.
Dataset format: * images are 4-channel GeoTIFFs * labels are singular float values
Dataset labels: * tree cover * elevation * population density
If you use this dataset in your research, please cite the following paper:
New in version 0.3.
- __init__(root='data', split='train', labels=['housing', 'income', 'roads', 'nightlights', 'population', 'elevation', 'treecover'], transforms=None, download=False, checksum=False)[source]¶
Initialize a new USAVars dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – train/val/test split to load
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if invalid labels are provided
ImportError – if pandas is not installed
RuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
Vaihingen¶
- class torchgeo.datasets.Vaihingen2D(root='data', split='train', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
Vaihingen 2D Semantic Segmentation dataset.
The Vaihingen dataset is a dataset for urban semantic segmentation used in the 2D Semantic Labeling Contest - Vaihingen. This dataset uses the “ISPRS_semantic_labeling_Vaihingen.zip” and “ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE.zip” files to create the train/test sets used in the challenge. The dataset can be requested at the challenge homepage. Note, the server contains additional data for 3D Semantic Labeling which are currently not supported.
Dataset format:
images are 3-channel RGB geotiffs
masks are 3-channel geotiffs with unique RGB values representing the class
Dataset classes:
Clutter/background
Impervious surfaces
Building
Low Vegetation
Tree
Car
If you use this dataset in your research, please cite the following paper:
New in version 0.2.
- __init__(root='data', split='train', transforms=None, checksum=False)[source]¶
Initialize a new Vaihingen2D dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
NWPU VHR-10¶
- class torchgeo.datasets.VHR10(root='data', split='positive', transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
NWPU VHR-10 dataset.
Northwestern Polytechnical University (NWPU) very-high-resolution ten-class (VHR-10) remote sensing image dataset.
Consists of 800 VHR optical remote sensing images, where 715 color images were acquired from Google Earth with the spatial resolution ranging from 0.5 to 2 m, and 85 pansharpened color infrared (CIR) images were acquired from Vaihingen data with a spatial resolution of 0.08 m.
The data set is divided into two sets:
Positive image set (650 images) which contains at least one target in an image
Negative image set (150 images) does not contain any targets
The positive image set consists of objects from ten classes:
Airplanes (757)
Ships (302)
Storage tanks (655)
Baseball diamonds (390)
Tennis courts (524)
Basketball courts (159)
Ground track fields (163)
Harbors (224)
Bridges (124)
Vehicles (477)
Includes object detection bounding boxes from original paper and instance segmentation masks from follow-up publications. If you use this dataset in your research, please cite the following papers:
Note
This dataset requires the following additional libraries to be installed:
pycocotools to load the
annotations.json
file for the “positive” image setrarfile to extract the dataset, which is stored in a RAR file
- __init__(root='data', split='positive', transforms=None, download=False, checksum=False)[source]¶
Initialize a new VHR-10 dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “postive” or “negative”
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
AssertionError – if
split
argument is invalidRuntimeError – if
download=False
and data is not found, or checksums don’t match
xView2¶
- class torchgeo.datasets.XView2(root='data', split='train', transforms=None, checksum=False)¶
Bases:
NonGeoDataset
xView2 dataset.
The xView2 dataset is a dataset for building disaster change detection. This dataset object uses the “Challenge training set (~7.8 GB)” and “Challenge test set (~2.6 GB)” data from the xView2 website as the train and test splits. Note, the xView2 website contains other data under the xView2 umbrella that are _not_ included here. E.g. the “Tier3 training data”, the “Challenge holdout set”, and the “full data”.
Dataset format:
images are three-channel pngs
masks are single-channel pngs where the pixel values represent the class
Dataset classes:
background
no damage
minor damage
major damage
destroyed
If you use this dataset in your research, please cite the following paper:
New in version 0.2.
- __init__(root='data', split='train', transforms=None, checksum=False)[source]¶
Initialize a new xView2 dataset instance.
- Parameters
root (str) – root directory where dataset can be found
split (str) – one of “train” or “test”
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
ZueriCrop¶
- class torchgeo.datasets.ZueriCrop(root='data', bands=('NIR', 'B03', 'B02', 'B04', 'B05', 'B06', 'B07', 'B11', 'B12'), transforms=None, download=False, checksum=False)¶
Bases:
NonGeoDataset
ZueriCrop dataset.
The ZueriCrop dataset is a dataset for time-series instance segmentation of crops.
Dataset features:
Sentinel-2 multispectral imagery
instance masks of 48 crop categories
nine multispectral bands
116k images with 10 m per pixel resolution (24x24 px)
~28k time-series containing 142 images each
Dataset format:
single hdf5 dataset containing images, semantic masks, and instance masks
data is parsed into images and instance masks, boxes, and labels
one mask per time-series
Dataset classes:
48 fine-grained hierarchical crop categories
If you use this dataset in your research, please cite the following paper:
Note
This dataset requires the following additional library to be installed:
h5py to load the dataset
- __init__(root='data', bands=('NIR', 'B03', 'B02', 'B04', 'B05', 'B06', 'B07', 'B11', 'B12'), transforms=None, download=False, checksum=False)[source]¶
Initialize a new ZueriCrop dataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
download (bool) – if True, download dataset and store it in the root directory
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)
- Raises
RuntimeError – if
download=False
and data is not found, or checksums don’t match
- __len__()[source]¶
Return the number of data points in the dataset.
- Returns
length of the dataset
- Return type
Base Classes¶
If you want to write your own custom dataset, you can extend one of these abstract base classes.
GeoDataset¶
- class torchgeo.datasets.GeoDataset(transforms=None)¶
Bases:
Dataset
[Dict
[str
,Any
]],ABC
Abstract base class for datasets containing geospatial information.
Geospatial information includes things like:
coordinates (latitude, longitude)
resolution
GeoDataset
is a special class of datasets. UnlikeNonGeoDataset
, the presence of geospatial information allows two or more datasets to be combined based on latitude/longitude. This allows users to do things like:Combine image and target labels and sample from both simultaneously (e.g. Landsat and CDL)
Combine datasets for multiple image sources for multimodal learning or data fusion (e.g. Landsat and Sentinel)
These combinations require that all queries are present in both datasets, and can be combined using an
IntersectionDataset
:dataset = landsat & cdl
Users may also want to:
Combine datasets for multiple image sources and treat them as equivalent (e.g. Landsat 7 and Landsat 8)
Combine datasets for disparate geospatial locations (e.g. Chesapeake NY and PA)
These combinations require that all queries are present in at least one dataset, and can be combined using a
UnionDataset
:dataset = landsat7 | landsat8
- __and__(other)[source]¶
Take the intersection of two
GeoDataset
.- Parameters
other (GeoDataset) – another dataset
- Returns
a single dataset
- Raises
ValueError – if other is not a
GeoDataset
- Return type
New in version 0.2.
- abstract __getitem__(query)[source]¶
Retrieve image/mask and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of image/mask and metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __len__()[source]¶
Return the number of files in the dataset.
- Returns
length of the dataset
- Return type
- __or__(other)[source]¶
Take the union of two GeoDatasets.
- Parameters
other (GeoDataset) – another dataset
- Returns
a single dataset
- Raises
ValueError – if other is not a
GeoDataset
- Return type
New in version 0.2.
- __str__()[source]¶
Return the informal string representation of the object.
- Returns
informal string representation
- Return type
- property bounds: BoundingBox¶
Bounds of the index.
- Returns
(minx, maxx, miny, maxy, mint, maxt) of the dataset
- property crs: CRS¶
coordinate reference system (CRS) for the dataset.
- Returns
New in version 0.2.
RasterDataset¶
- class torchgeo.datasets.RasterDataset(root, crs=None, res=None, transforms=None, cache=True)¶
Bases:
GeoDataset
Abstract base class for
GeoDataset
stored as raster files.- __getitem__(query)[source]¶
Retrieve image/mask and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of image/mask and metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root, crs=None, res=None, transforms=None, cache=True)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (Optional[float]) – resolution of the dataset in units of CRS (defaults to the resolution of the first file found)
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes an input sample and returns a transformed version
cache (bool) – if True, cache file handle to speed up repeated sampling
- Raises
FileNotFoundError – if no files are found in
root
VectorDataset¶
- class torchgeo.datasets.VectorDataset(root='data', crs=None, res=0.0001, transforms=None)¶
Bases:
GeoDataset
Abstract base class for
GeoDataset
stored as vector files.- __getitem__(query)[source]¶
Retrieve image/mask and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of image/mask and metadata at that index
- Raises
IndexError – if query is not found in the index
- Return type
- __init__(root='data', crs=None, res=0.0001, transforms=None)[source]¶
Initialize a new Dataset instance.
- Parameters
root (str) – root directory where dataset can be found
crs (Optional[CRS]) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)
res (float) – resolution of the dataset in units of CRS
transforms (Optional[Callable[[Dict[str, Any]], Dict[str, Any]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
- Raises
FileNotFoundError – if no files are found in
root
NonGeoDataset¶
- class torchgeo.datasets.NonGeoDataset¶
Bases:
Dataset
[Dict
[str
,Any
]],ABC
Abstract base class for datasets lacking geospatial information.
This base class is designed for datasets with pre-defined image chips.
- abstract __getitem__(index)[source]¶
Return an index within the dataset.
- Parameters
index (int) – index to return
- Returns
data and labels at that index
- Raises
IndexError – if index is out of range of the dataset
- Return type
NonGeoClassificationDataset¶
- class torchgeo.datasets.NonGeoClassificationDataset(root, transforms=None, loader=<function default_loader>, is_valid_file=None)¶
Bases:
NonGeoDataset
,ImageFolder
Abstract base class for classification datasets lacking geospatial information.
This base class is designed for datasets with pre-defined image chips which are separated into separate folders per class.
- __init__(root, transforms=None, loader=<function default_loader>, is_valid_file=None)[source]¶
Initialize a new NonGeoClassificationDataset instance.
- Parameters
root (str) – root directory where dataset can be found
transforms (Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]]) – a function/transform that takes input sample and its target as entry and returns a transformed version
loader (Optional[Callable[[str], Any]]) – a callable function which takes as input a path to an image and returns a PIL Image or numpy array
is_valid_file (Optional[Callable[[str], bool]]) – A function that takes the path of an Image file and checks if the file is a valid file
IntersectionDataset¶
- class torchgeo.datasets.IntersectionDataset(dataset1, dataset2, collate_fn=<function concat_samples>)¶
Bases:
GeoDataset
Dataset representing the intersection of two GeoDatasets.
This allows users to do things like:
Combine image and target labels and sample from both simultaneously (e.g. Landsat and CDL)
Combine datasets for multiple image sources for multimodal learning or data fusion (e.g. Landsat and Sentinel)
These combinations require that all queries are present in both datasets, and can be combined using an
IntersectionDataset
:dataset = landsat & cdl
New in version 0.2.
- __getitem__(query)[source]¶
Retrieve image and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of data/labels and metadata at that index
- Raises
IndexError – if query is not within bounds of the index
- Return type
- __init__(dataset1, dataset2, collate_fn=<function concat_samples>)[source]¶
Initialize a new Dataset instance.
- Parameters
dataset1 (GeoDataset) – the first dataset
dataset2 (GeoDataset) – the second dataset
collate_fn (Callable[[Sequence[Dict[str, Any]]], Dict[str, Any]]) – function used to collate samples
- Raises
ValueError – if either dataset is not a
GeoDataset
UnionDataset¶
- class torchgeo.datasets.UnionDataset(dataset1, dataset2, collate_fn=<function merge_samples>)¶
Bases:
GeoDataset
Dataset representing the union of two GeoDatasets.
This allows users to do things like:
Combine datasets for multiple image sources and treat them as equivalent (e.g. Landsat 7 and Landsat 8)
Combine datasets for disparate geospatial locations (e.g. Chesapeake NY and PA)
These combinations require that all queries are present in at least one dataset, and can be combined using a
UnionDataset
:dataset = landsat7 | landsat8
New in version 0.2.
- __getitem__(query)[source]¶
Retrieve image and metadata indexed by query.
- Parameters
query (BoundingBox) – (minx, maxx, miny, maxy, mint, maxt) coordinates to index
- Returns
sample of data/labels and metadata at that index
- Raises
IndexError – if query is not within bounds of the index
- Return type
- __init__(dataset1, dataset2, collate_fn=<function merge_samples>)[source]¶
Initialize a new Dataset instance.
- Parameters
dataset1 (GeoDataset) – the first dataset
dataset2 (GeoDataset) – the second dataset
collate_fn (Callable[[Sequence[Dict[str, Any]]], Dict[str, Any]]) – function used to collate samples
- Raises
ValueError – if either dataset is not a
GeoDataset
Utilities¶
- class torchgeo.datasets.BoundingBox(minx, maxx, miny, maxy, mint, maxt)¶
Bases:
object
Data class for indexing spatiotemporal data.
- __and__(other)[source]¶
The intersection operator.
- Parameters
other (BoundingBox) – another bounding box
- Returns
the intersection of self and other
- Raises
ValueError – if self and other do not intersect
- Return type
New in version 0.2.
- __contains__(other)[source]¶
Whether or not other is within the bounds of this bounding box.
- Parameters
other (BoundingBox) – another bounding box
- Returns
True if other is within this bounding box, else False
- Return type
New in version 0.2.
- __delattr__(name)¶
Implement delattr(self, name).
- __eq__(other)¶
Return self==value.
- __hash__()¶
Return hash(self).
- __init__(minx, maxx, miny, maxy, mint, maxt)¶
- __or__(other)[source]¶
The union operator.
- Parameters
other (BoundingBox) – another bounding box
- Returns
the minimum bounding box that contains both self and other
- Return type
New in version 0.2.
- __post_init__()[source]¶
Validate the arguments passed to
__init__()
.- Raises
ValueError – if bounding box is invalid (minx > maxx, miny > maxy, or mint > maxt)
New in version 0.2.
- __repr__()¶
Return repr(self).
- __setattr__(name, value)¶
Implement setattr(self, name, value).
- __weakref__¶
list of weak references to the object (if defined)
- property area: float¶
Area of bounding box.
Area is defined as spatial area.
- Returns
area
New in version 0.3.
- intersects(other)[source]¶
Whether or not two bounding boxes intersect.
- Parameters
other (BoundingBox) – another bounding box
- Returns
True if bounding boxes intersect, else False
- Return type
Collation Functions¶
- torchgeo.datasets.stack_samples(samples)¶
Stack a list of samples along a new axis.
Useful for forming a mini-batch of samples to pass to
torch.utils.data.DataLoader
.- Parameters
- Returns
a single sample
- Return type
New in version 0.2.
- torchgeo.datasets.concat_samples(samples)¶
Concatenate a list of samples along an existing axis.
Useful for joining samples in a
torchgeo.datasets.IntersectionDataset
.- Parameters
- Returns
a single sample
- Return type
New in version 0.2.
- torchgeo.datasets.merge_samples(samples)¶
Merge a list of samples.
Useful for joining samples in a
torchgeo.datasets.UnionDataset
.- Parameters
- Returns
a single sample
- Return type
New in version 0.2.
- torchgeo.datasets.unbind_samples(sample)¶
Reverse of
stack_samples()
.Useful for turning a mini-batch of samples into a list of samples. These individual samples can then be plotted using a dataset’s
plot
method.- Parameters
- Returns
list of samples
- Return type
New in version 0.2.