Source code for torchgeo.datasets.esri2020

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""Esri 2020 Land Cover Dataset."""

import glob
import os
from import Iterable
from typing import Any, Callable, Optional, Union

import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from import CRS

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

[docs]class Esri2020(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: 0. No Data 1. Water 2. Trees 3. Grass 4. Flooded Vegetation 5. Crops 6. Scrub/Shrub 7. Built Area 8. Bare Ground 9. Snow/Ice 10. Clouds A more detailed explanation of the invidual classes can be found `here <>`_. If you use this dataset please cite the following paper: * .. versionadded:: 0.3 """ is_image = False filename_glob = "*_20200101-20210101.*" filename_regex = r"""^ (?P<id>[0-9][0-9][A-Z]) _(?P<date>\d{8}) -(?P<processing_date>\d{8}) """ zipfile = "" md5 = "4932855fcd00735a34b74b1f87db3df0" url = ( "" "" )
[docs] def __init__( self, paths: Union[str, Iterable[str]] = "data", crs: Optional[CRS] = None, res: Optional[float] = None, transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = 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) 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: FileNotFoundError: if no files are found in ``paths`` RuntimeError: if ``download=False`` but dataset is missing or checksum fails .. versionchanged:: 0.5 *root* was renamed to *paths*. """ self.paths = paths = download self.checksum = checksum self._verify() super().__init__(paths, crs, res, transforms=transforms, cache=cache)
def _verify(self) -> None: """Verify the integrity of the dataset. Raises: RuntimeError: if ``download=False`` but dataset is missing or checksum fails """ # Check if the extracted file already exists if self.files: return # Check if the zip files have already been downloaded assert isinstance(self.paths, str) pathname = os.path.join(self.paths, self.zipfile) if glob.glob(pathname): self._extract() return # Check if the user requested to download the dataset if not raise RuntimeError( f"Dataset not found in `paths={self.paths!r}` and `download=False`, " "either specify a different `paths` or use `download=True` " "to automatically download the dataset." ) # Download the dataset self._download() self._extract() def _download(self) -> None: """Download the dataset.""" download_url(self.url, self.paths, filename=self.zipfile, md5=self.md5) def _extract(self) -> None: """Extract the dataset.""" assert isinstance(self.paths, str) extract_archive(os.path.join(self.paths, self.zipfile))
[docs] def plot( self, sample: dict[str, Any], show_titles: bool = True, suptitle: Optional[str] = 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: prediction = sample["prediction"].squeeze() ncols = 2 fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(4 * ncols, 4)) if showing_predictions: axs[0].imshow(mask) axs[0].axis("off") axs[1].imshow(prediction) axs[1].axis("off") if show_titles: axs[0].set_title("Mask") axs[1].set_title("Prediction") else: axs.imshow(mask) axs.axis("off") if show_titles: axs.set_title("Mask") if suptitle is not None: plt.suptitle(suptitle) return fig

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