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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

import matplotlib.pyplot as plt
import torch
from matplotlib.figure import Figure
from rasterio.crs import CRS

from .geo import RasterDataset
from .utils import BoundingBox, DatasetNotFoundError, download_url, extract_archive


[docs]class CDL(RasterDataset): """Cropland Data Layer (CDL) dataset. The `Cropland Data Layer <https://data.nal.usda.gov/dataset/cropscape-cropland-data-layer>`__, 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#Section1_14.0 """ # noqa: E501 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" # noqa: E501 md5s = { 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 = { 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: str | Iterable[str] = "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) 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) 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

© Copyright 2021, Microsoft Corporation. Revision acad7d47.

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