Source code for torchgeo.datasets.nccm
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Northeastern China Crop Map Dataset."""
from collections.abc import Callable, Iterable
from typing import Any
import matplotlib.pyplot as plt
import torch
from matplotlib.figure import Figure
from rasterio.crs import CRS
from .errors import DatasetNotFoundError
from .geo import RasterDataset
from .utils import BoundingBox, download_url
[docs]class NCCM(RasterDataset):
"""The Northeastern China Crop Map Dataset.
Link: https://www.nature.com/articles/s41597-021-00827-9
This dataset produced annual 10-m crop maps of the
major crops (maize, soybean, and rice)
in Northeast China from 2017 to 2019, using hierarchial mapping strategies,
random forest classifiers, interpolated and
smoothed 10-day Sentinel-2 time series data and
optimized features from spectral, temporal and
textural characteristics of the land surface.
The resultant maps have high overall accuracies (OA)
based on ground truth data. The dataset contains information
specific to three years: 2017, 2018, 2019.
The dataset contains 5 classes:
0. paddy rice
1. maize
2. soybean
3. others crops and lands
4. nodata
Dataset format:
* Three .TIF files containing the labels
* JavaScript code to download images from the dataset.
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1038/s41597-021-00827-9
.. versionadded:: 0.6
"""
filename_regex = r'CDL(?P<date>\d{4})_clip'
filename_glob = 'CDL*.*'
date_format = '%Y'
is_image = False
urls = {
2019: 'https://figshare.com/ndownloader/files/25070540',
2018: 'https://figshare.com/ndownloader/files/25070624',
2017: 'https://figshare.com/ndownloader/files/25070582',
}
md5s = {
2019: '0d062bbd42e483fdc8239d22dba7020f',
2018: 'b3bb4894478d10786aa798fb11693ec1',
2017: 'd047fbe4a85341fa6248fd7e0badab6c',
}
fnames = {
2019: 'CDL2019_clip.tif',
2018: 'CDL2018_clip1.tif',
2017: 'CDL2017_clip.tif',
}
cmap = {
0: (0, 255, 0, 255),
1: (255, 0, 0, 255),
2: (255, 255, 0, 255),
3: (128, 128, 128, 255),
15: (255, 255, 255, 255),
}
[docs] def __init__(
self,
paths: str | Iterable[str] = 'data',
crs: CRS | None = None,
res: float | None = None,
years: list[int] = [2019],
transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
cache: bool = True,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new dataset.
Args:
paths: one or more root directories to search or files to load
crs: :term:`coordinate reference system (CRS)` to warp to
(defaults to the CRS of the first file found)
res: resolution of the dataset in units of CRS
(defaults to the resolution of the first file found)
years: list of years for which to use nccm layers
transforms: a function/transform that takes an input sample
and returns a transformed version
cache: if True, cache file handle to speed up repeated sampling
download: if True, download dataset and store it in the root directory
checksum: if True, check the MD5 after downloading files (may be slow)
Raises:
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
assert set(years) <= self.md5s.keys(), (
'NCCM data product only exists for the following years: '
f'{list(self.md5s.keys())}.'
)
self.paths = paths
self.years = years
self.download = download
self.checksum = checksum
self.ordinal_map = torch.full((max(self.cmap.keys()) + 1,), 4, dtype=self.dtype)
self.ordinal_cmap = torch.zeros((5, 4), dtype=torch.uint8)
self._verify()
super().__init__(paths, crs, res, transforms=transforms, cache=cache)
for i, (k, v) in enumerate(self.cmap.items()):
self.ordinal_map[k] = i
self.ordinal_cmap[i] = torch.tensor(v)
[docs] def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
"""Retrieve mask and metadata indexed by query.
Args:
query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index
Returns:
sample of mask and metadata at that index
Raises:
IndexError: if query is not found in the index
"""
sample = super().__getitem__(query)
sample['mask'] = self.ordinal_map[sample['mask']]
return sample
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the files already exist
if self.files:
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
def _download(self) -> None:
"""Download the dataset."""
for year in self.years:
download_url(
self.urls[year],
self.paths,
filename=self.fnames[year],
md5=self.md5s[year] if self.checksum else None,
)
[docs] def plot(
self,
sample: dict[str, Any],
show_titles: bool = True,
suptitle: str | None = None,
) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample returned by :meth:`NCCM.__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional string to use as a suptitle
Returns:
a matplotlib Figure with the rendered sample
"""
mask = sample['mask'].squeeze()
ncols = 1
showing_predictions = 'prediction' in sample
if showing_predictions:
pred = sample['prediction'].squeeze()
ncols = 2
fig, axs = plt.subplots(
nrows=1, ncols=ncols, figsize=(ncols * 4, 4), squeeze=False
)
axs[0, 0].imshow(self.ordinal_cmap[mask], interpolation='none')
axs[0, 0].axis('off')
if show_titles:
axs[0, 0].set_title('Mask')
if showing_predictions:
axs[0, 1].imshow(self.ordinal_cmap[pred], interpolation='none')
axs[0, 1].axis('off')
if show_titles:
axs[0, 1].set_title('Prediction')
if suptitle is not None:
plt.suptitle(suptitle)
return fig
