Source code for torchgeo.datasets.cdl
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""CDL dataset."""
import os
from collections.abc import Callable, Iterable
from typing import Any, ClassVar
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, Path, download_url, extract_archive
[docs]class CDL(RasterDataset):
"""Cropland Data Layer (CDL) dataset.
The `Cropland Data Layer
<https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>`__, hosted on
`CropScape <https://nassgeodata.gmu.edu/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.
The dataset contains 134 classes, for a description of the classes see the
xls file at the top of
`this page <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php>`_.
If you use this dataset in your research, please cite it using the following format:
* https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php#what.1
"""
filename_glob = '*_30m_cdls.tif'
filename_regex = r"""
^(?P<date>\d+)
_30m_cdls\..*$
"""
zipfile_glob = '*_30m_cdls.zip'
date_format = '%Y'
is_image = False
url = 'https://www.nass.usda.gov/Research_and_Science/Cropland/Release/datasets/{}_30m_cdls.zip'
md5s: ClassVar[dict[int, str]] = {
2023: '8c7685d6278d50c554f934b16a6076b7',
2022: '754cf50670cdfee511937554785de3e6',
2021: '27606eab08fe975aa138baad3e5dfcd8',
2020: '483ee48c503aa81b684225179b402d42',
2019: 'a5168a2fc93acbeaa93e24eee3d8c696',
2018: '4ad0d7802a9bb751685eb239b0fa8609',
2017: 'd173f942a70f94622f9b8290e7548684',
2016: 'fddc5dff0bccc617d70a12864c993e51',
2015: '2e92038ab62ba75e1687f60eecbdd055',
2014: '50bdf9da84ebd0457ddd9e0bf9bbcc1f',
2013: '7be66c650416dc7c4a945dd7fd93c5b7',
2012: '286504ff0512e9fe1a1975c635a1bec2',
2011: '517bad1a99beec45d90abb651fb1f0e3',
2010: '98d354c5a62c9e3e40ccadce265c721c',
2009: '663c8a5fdd92ebfc0d6bee008586d19a',
2008: '0610f2f17ab60a9fbb3baeb7543993a4',
}
cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
0: (0, 0, 0, 255),
1: (255, 211, 0, 255),
2: (255, 37, 37, 255),
3: (0, 168, 226, 255),
4: (255, 158, 9, 255),
5: (37, 111, 0, 255),
6: (255, 255, 0, 255),
10: (111, 166, 0, 255),
11: (0, 175, 73, 255),
12: (222, 166, 9, 255),
13: (222, 166, 9, 255),
14: (124, 211, 255, 255),
21: (226, 0, 124, 255),
22: (137, 96, 83, 255),
23: (217, 181, 107, 255),
24: (166, 111, 0, 255),
25: (213, 158, 188, 255),
26: (111, 111, 0, 255),
27: (171, 0, 124, 255),
28: (160, 88, 137, 255),
29: (111, 0, 73, 255),
30: (213, 158, 188, 255),
31: (209, 255, 0, 255),
32: (124, 153, 255, 255),
33: (213, 213, 0, 255),
34: (209, 255, 0, 255),
35: (0, 175, 73, 255),
36: (255, 166, 226, 255),
37: (166, 241, 139, 255),
38: (0, 175, 73, 255),
39: (213, 158, 188, 255),
41: (168, 0, 226, 255),
42: (166, 0, 0, 255),
43: (111, 37, 0, 255),
44: (0, 175, 73, 255),
45: (175, 124, 255, 255),
46: (111, 37, 0, 255),
47: (255, 102, 102, 255),
48: (255, 102, 102, 255),
49: (255, 204, 102, 255),
50: (255, 102, 102, 255),
51: (0, 175, 73, 255),
52: (0, 222, 175, 255),
53: (83, 255, 0, 255),
54: (241, 162, 120, 255),
55: (255, 102, 102, 255),
56: (0, 175, 73, 255),
57: (124, 211, 255, 255),
58: (232, 190, 255, 255),
59: (175, 255, 222, 255),
60: (0, 175, 73, 255),
61: (190, 190, 120, 255),
63: (147, 204, 147, 255),
64: (198, 213, 158, 255),
65: (204, 190, 162, 255),
66: (255, 0, 255, 255),
67: (255, 143, 171, 255),
68: (185, 0, 79, 255),
69: (111, 69, 137, 255),
70: (0, 120, 120, 255),
71: (175, 153, 111, 255),
72: (255, 255, 124, 255),
74: (181, 111, 92, 255),
75: (0, 166, 130, 255),
76: (232, 213, 175, 255),
77: (175, 153, 111, 255),
81: (241, 241, 241, 255),
82: (153, 153, 153, 255),
83: (73, 111, 162, 255),
87: (124, 175, 175, 255),
88: (232, 255, 190, 255),
92: (0, 255, 255, 255),
111: (73, 111, 162, 255),
112: (211, 226, 249, 255),
121: (153, 153, 153, 255),
122: (153, 153, 153, 255),
123: (153, 153, 153, 255),
124: (153, 153, 153, 255),
131: (204, 190, 162, 255),
141: (147, 204, 147, 255),
142: (147, 204, 147, 255),
143: (147, 204, 147, 255),
152: (198, 213, 158, 255),
176: (232, 255, 190, 255),
190: (124, 175, 175, 255),
195: (124, 175, 175, 255),
204: (0, 255, 139, 255),
205: (213, 158, 188, 255),
206: (255, 102, 102, 255),
207: (255, 102, 102, 255),
208: (255, 102, 102, 255),
209: (255, 102, 102, 255),
210: (255, 143, 171, 255),
211: (51, 73, 51, 255),
212: (226, 111, 37, 255),
213: (255, 102, 102, 255),
214: (255, 102, 102, 255),
215: (102, 153, 77, 255),
216: (255, 102, 102, 255),
217: (175, 153, 111, 255),
218: (255, 143, 171, 255),
219: (255, 102, 102, 255),
220: (255, 143, 171, 255),
221: (255, 102, 102, 255),
222: (255, 102, 102, 255),
223: (255, 143, 171, 255),
224: (0, 175, 73, 255),
225: (255, 211, 0, 255),
226: (255, 211, 0, 255),
227: (255, 102, 102, 255),
228: (255, 211, 0, 255),
229: (255, 102, 102, 255),
230: (137, 96, 83, 255),
231: (255, 102, 102, 255),
232: (255, 37, 37, 255),
233: (226, 0, 124, 255),
234: (255, 158, 9, 255),
235: (255, 158, 9, 255),
236: (166, 111, 0, 255),
237: (255, 211, 0, 255),
238: (166, 111, 0, 255),
239: (37, 111, 0, 255),
240: (37, 111, 0, 255),
241: (255, 211, 0, 255),
242: (0, 0, 153, 255),
243: (255, 102, 102, 255),
244: (255, 102, 102, 255),
245: (255, 102, 102, 255),
246: (255, 102, 102, 255),
247: (255, 102, 102, 255),
248: (255, 102, 102, 255),
249: (255, 102, 102, 255),
250: (255, 102, 102, 255),
254: (37, 111, 0, 255),
}
[docs] def __init__(
self,
paths: Path | Iterable[Path] = 'data',
crs: CRS | None = None,
res: float | None = None,
years: list[int] = [2023],
classes: list[int] = list(cmap.keys()),
transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
cache: bool = True,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new Dataset instance.
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 cdl layer
classes: list of classes to include, the rest will be mapped to 0
(defaults to all classes)
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 of the downloaded files (may be slow)
Raises:
AssertionError: if ``years`` or ``classes`` are invalid
DatasetNotFoundError: If dataset is not found and *download* is False.
.. versionadded:: 0.5
The *years* and *classes* parameters.
.. versionchanged:: 0.5
*root* was renamed to *paths*.
"""
assert set(years) <= self.md5s.keys(), (
'CDL data product only exists for the following years: '
f'{list(self.md5s.keys())}.'
)
assert (
set(classes) <= self.cmap.keys()
), f'Only the following classes are valid: {list(self.cmap.keys())}.'
assert 0 in classes, 'Classes must include the background class: 0'
self.paths = paths
self.years = years
self.classes = classes
self.download = download
self.checksum = checksum
self.ordinal_map = torch.zeros(max(self.cmap.keys()) + 1, dtype=self.dtype)
self.ordinal_cmap = torch.zeros((len(self.classes), 4), dtype=torch.uint8)
self._verify()
super().__init__(paths, crs, res, transforms=transforms, cache=cache)
# Map chosen classes to ordinal numbers, all others mapped to background class
for v, k in enumerate(self.classes):
self.ordinal_map[k] = v
self.ordinal_cmap[v] = torch.tensor(self.cmap[k])
[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 extracted files already exist
if self.files:
return
# Check if the zip files have already been downloaded
exists = []
assert isinstance(self.paths, str | os.PathLike)
for year in self.years:
pathname = os.path.join(
self.paths, self.zipfile_glob.replace('*', str(year))
)
if os.path.exists(pathname):
exists.append(True)
self._extract()
else:
exists.append(False)
if all(exists):
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
for year in self.years:
download_url(
self.url.format(year),
self.paths,
md5=self.md5s[year] if self.checksum else None,
)
def _extract(self) -> None:
"""Extract the dataset."""
assert isinstance(self.paths, str | os.PathLike)
for year in self.years:
zipfile_name = self.zipfile_glob.replace('*', str(year))
pathname = os.path.join(self.paths, zipfile_name)
extract_archive(pathname, self.paths)
[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:`RasterDataset.__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
.. versionchanged:: 0.3
Method now takes a sample dict, not a Tensor. Additionally, possible to
show subplot titles and/or use a custom suptitle.
"""
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