Source code for torchgeo.datasets.nlcd
# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""NLCD 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 pyproj import CRS
from .errors import DatasetNotFoundError
from .geo import RasterDataset
from .utils import GeoSlice, Path, download_url
[docs]class NLCD(RasterDataset):
"""Annual National Land Cover Database (NLCD) dataset.
The `Annual NLCD products
<https://www.usgs.gov/centers/eros/science/annual-national-land-cover-database>`_
is an annual land cover product for the conterminous U.S. initially covering the period
from 1985 to 2023. The product is a joint effort between the United States Geological Survey
(`USGS <https://www.usgs.gov/>`_) and the Multi-Resolution Land Characteristics
Consortium (`MRLC <https://www.mrlc.gov/>`_).
The dataset contains the following 17 classes:
0. Background
#. Open Water
#. Perennial Ice/Snow
#. Developed, Open Space
#. Developed, Low Intensity
#. Developed, Medium Intensity
#. Developed, High Intensity
#. Barren Land (Rock/Sand/Clay)
#. Deciduous Forest
#. Evergreen Forest
#. Mixed Forest
#. Shrub/Scrub
#. Grassland/Herbaceous
#. Pasture/Hay
#. Cultivated Crops
#. Woody Wetlands
#. Emergent Herbaceous Wetlands
Detailed descriptions of the classes can be found
`here <https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description>`__.
Dataset format:
* single channel .img file with integer class labels
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.5066/P94UXNTS
.. versionadded:: 0.5
"""
filename_glob = 'Annual_NLCD_LndCov_*_CU_C1V0.tif'
filename_regex = r'Annual_NLCD_LndCov_(?P<date>\d{4})_CU_C1V0\.tif'
date_format = '%Y'
is_image = False
url = 'https://s3-us-west-2.amazonaws.com/mrlc/Annual_NLCD_LndCov_{}_CU_C1V0.tif'
md5s: ClassVar[dict[int, str]] = {
1985: 'a2e1c5f0b34e9b15a63a9dc10e8d3ec2',
1986: 'da1d08ca51ac43abc14711c8d6139f1d',
1987: '2cb85e8f077c227605cd7bac62a72a75',
1988: 'b20fb987cc30926d2d125d045e02626d',
1989: 'dbe851cbea34d0a57c2a94eb745a1267',
1990: '1927e0e040b9ff513ff039749b64919b',
1991: 'eca73474843d6c58693eba62d70e507c',
1992: '8beda41ba79000f55a8e9358ba3fa5a4',
1993: '1a023552967cdac1111e9968ea62c879',
1994: 'acc30ce4f6cdd78af5f7887d17ac4de3',
1995: 'f728e8fc231b2e8e74a14201f500543a',
1996: 'd2580904244f89b20d6258150fbf4161',
1997: 'fec4e08032e162f2cc7dbe019d042609',
1998: '87ea19434de96ea99cd5d7991042816c',
1999: 'd4133737f20e75f3bd3a5baa32a668da',
2000: 'e20b61bb2e7f4034a33c9fd536798a01',
2001: 'b1f46ace9aedd17a89efab489cb67bc3',
2002: '57bf60d7cd473096af3bb125391bde63',
2003: '5e346854da9abf739152e85fee4c7aff',
2004: '13136f271f53a454358eb7ec12bda686',
2005: 'f00b66b57a23eb49a077e88704964a91',
2006: '074ba90de5e62a37a5f001b7572f6baa',
2007: 'cdef29a191cf165baaae80857ce5a980',
2008: 'da907c76a1f12739333148504fd111c9',
2009: '47890b306b875e681990b3db0c709da3',
2010: '9a81f405f9e2f45d581078afd53c2d4b',
2011: '13f4ef40b204aa1108dc0599d9546701',
2012: '66b33146f9a9d9491be10c59c51e3e33',
2013: 'f8d230f7dea493c47fbc74984ff856cc',
2014: '68eb07ce86c1f7c2546ec43c2f9f7029',
2015: 'f5a1b59fe54a70752f544c06cb965be4',
2016: 'f0c2e74824fc281a57821e28e2c7fe6e',
2017: 'a0aa8be0ed7d637f0f88f26d3742b20e',
2018: 'a01f31547837ff1dfec1aba07b89bbec',
2019: 'fa738201cddc1393dac4383b6ce2561a',
2020: 'aa8f51690c7b01f3b3b413be9a7c36d6',
2021: '47fc1794a64704a918b6ad586df4267c',
2022: '11359748229e138cde971947864104a4',
2023: '498ff8a512d32fe905720796fdb7fd52',
}
cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
0: (0, 0, 0, 0),
11: (70, 107, 159, 255),
12: (209, 222, 248, 255),
21: (222, 197, 197, 255),
22: (217, 146, 130, 255),
23: (235, 0, 0, 255),
24: (171, 0, 0, 255),
31: (179, 172, 159, 255),
41: (104, 171, 95, 255),
42: (28, 95, 44, 255),
43: (181, 197, 143, 255),
52: (204, 184, 121, 255),
71: (223, 223, 194, 255),
81: (220, 217, 57, 255),
82: (171, 108, 40, 255),
90: (184, 217, 235, 255),
95: (108, 159, 184, 255),
}
[docs] def __init__(
self,
paths: Path | Iterable[Path] = 'data',
crs: CRS | None = None,
res: float | tuple[float, 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 in (xres, yres) format. If a
single float is provided, it is used for both the x and y resolution.
(defaults to the resolution of the first file found)
years: list of years for which to use nlcd 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 after downloading files (may be slow)
Raises:
AssertionError: if ``years`` or ``classes`` are invalid
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
assert set(years) <= self.md5s.keys(), (
'NLCD 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: GeoSlice) -> dict[str, Any]:
"""Retrieve input, target, and/or metadata indexed by spatiotemporal slice.
Args:
query: [xmin:xmax:xres, ymin:ymax:yres, tmin:tmax:tres] coordinates to index.
Returns:
Sample of input, target, and/or 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 TIFF files for the specified years have already been downloaded
exists = []
for year in self.years:
filename_year = self.filename_glob.replace('*', str(year), 1)
assert isinstance(self.paths, str | os.PathLike)
pathname = os.path.join(self.paths, filename_year)
if os.path.exists(pathname):
exists.append(True)
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()
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,
)
[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
"""
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
