Adding Esri 2020 Land Cover/Land Use Dataset (#390)

* Adding Esri 2020 dataset

* long url string

* requested changes

* Add period at end of sentence

Co-authored-by: Adam J. Stewart <[email protected]>

docs/api/datasets.rst

@@ -42,6 +42,11 @@ EnviroAtlas
 
 .. autoclass:: EnviroAtlas
 
+Esri2020
+^^^^^^^^
+
+.. autoclass:: Esri2020
+
 Landsat
 ^^^^^^^
 

tests/datasets/test_esri2020.py

@@ -0,0 +1,93 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+from pathlib import Path
+from typing import Generator
+
+import pytest
+import torch
+import torch.nn as nn
+from _pytest.monkeypatch import MonkeyPatch
+from rasterio.crs import CRS
+
+import torchgeo.datasets.utils
+from torchgeo.datasets import BoundingBox, Esri2020, IntersectionDataset, UnionDataset
+
+
+def download_url(url: str, root: str, *args: str, **kwargs: str) -> None:
+    shutil.copy(url, root)
+
+
+class TestEsri2020:
+    @pytest.fixture
+    def dataset(
+        self, monkeypatch: Generator[MonkeyPatch, None, None], tmp_path: Path
+    ) -> Esri2020:
+        monkeypatch.setattr(  # type: ignore[attr-defined]
+            torchgeo.datasets.esri2020, "download_url", download_url
+        )
+        zipfile = "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip"
+        monkeypatch.setattr(Esri2020, "zipfile", zipfile)  # type: ignore[attr-defined]
+
+        md5 = "4932855fcd00735a34b74b1f87db3df0"
+        monkeypatch.setattr(Esri2020, "md5", md5)  # type: ignore[attr-defined]
+        url = os.path.join(
+            "tests",
+            "data",
+            "esri2020",
+            "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip",
+        )
+        monkeypatch.setattr(Esri2020, "url", url)  # type: ignore[attr-defined]
+        root = str(tmp_path)
+        transforms = nn.Identity()  # type: ignore[attr-defined]
+        return Esri2020(root, transforms=transforms, download=True, checksum=True)
+
+    def test_already_downloaded(self, tmp_path: Path) -> None:
+        url = os.path.join(
+            "tests",
+            "data",
+            "esri2020",
+            "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip",
+        )
+        root = str(tmp_path)
+        shutil.copy(url, root)
+        Esri2020(root)
+
+    def test_getitem(self, dataset: Esri2020) -> None:
+        x = dataset[dataset.bounds]
+        assert isinstance(x, dict)
+        assert isinstance(x["crs"], CRS)
+        assert isinstance(x["mask"], torch.Tensor)
+
+    def test_already_extracted(self, dataset: Esri2020) -> None:
+        Esri2020(root=dataset.root, download=True)
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(RuntimeError, match="Dataset not found"):
+            Esri2020(str(tmp_path), checksum=True)
+
+    def test_and(self, dataset: Esri2020) -> None:
+        ds = dataset & dataset
+        assert isinstance(ds, IntersectionDataset)
+
+    def test_or(self, dataset: Esri2020) -> None:
+        ds = dataset | dataset
+        assert isinstance(ds, UnionDataset)
+
+    def test_plot(self, dataset: Esri2020) -> None:
+        query = dataset.bounds
+        x = dataset[query]
+        dataset.plot(x["mask"])
+
+    def test_url(self) -> None:
+        ds = Esri2020(os.path.join("tests", "data", "esri2020"))
+        assert "ai4edataeuwest.blob.core.windows.net" in ds.url
+
+    def test_invalid_query(self, dataset: Esri2020) -> None:
+        query = BoundingBox(0, 0, 0, 0, 0, 0)
+        with pytest.raises(
+            IndexError, match="query: .* not found in index with bounds:"
+        ):
+            dataset[query]

torchgeo/datasets/__init__.py

@@ -26,6 +26,7 @@
 from .cyclone import TropicalCycloneWindEstimation
 from .dfc2022 import DFC2022
 from .enviroatlas import EnviroAtlas
+from .esri2020 import Esri2020
 from .etci2021 import ETCI2021
 from .eurosat import EuroSAT
 from .fair1m import FAIR1M
@@ -96,6 +97,7 @@
     "ChesapeakeVA",
     "ChesapeakeWV",
     "ChesapeakeCVPR",
+    "Esri2020",
     "Landsat",
     "Landsat1",
     "Landsat2",

torchgeo/datasets/esri2020.py

@@ -0,0 +1,138 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""Esri 2020 Land Cover Dataset."""
+
+import abc
+import glob
+import os
+from typing import Any, Callable, Dict, Optional
+
+from rasterio.crs import CRS
+
+from .geo import RasterDataset
+from .utils import download_url, extract_archive
+
+
+class Esri2020(RasterDataset, abc.ABC):
+    """Esri 2020 Land Cover Dataset.
+
+    The `Esri 2020 Land Cover dataset
+    <https://www.arcgis.com/home/item.html?id=fc92d38533d440078f17678ebc20e8e2>`_
+    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 <https://www.arcgis.com/home/item.html?id=fc92d38533d440078f17678ebc20e8e2>`_.
+
+    If you use this dataset please cite the following paper:
+
+    * https://ieeexplore.ieee.org/document/9553499
+
+    .. 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 = "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip"
+    md5 = "4932855fcd00735a34b74b1f87db3df0"
+
+    url = (
+        "https://ai4edataeuwest.blob.core.windows.net/io-lulc/"
+        "io-lulc-model-001-v01-composite-v03-supercell-v02-clip-v01.zip"
+    )
+
+    def __init__(
+        self,
+        root: 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:
+            root: root directory where dataset can be found
+            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 ``root``
+            RuntimeError: if ``download=False`` but dataset is missing or checksum fails
+        """
+        self.root = root
+        self.download = download
+        self.checksum = checksum
+
+        self._verify()
+
+        super().__init__(root, crs, res, transforms, 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
+        pathname = os.path.join(self.root, "**", self.filename_glob)
+        if glob.glob(pathname):
+            return
+
+        # Check if the zip files have already been downloaded
+        pathname = os.path.join(self.root, self.zipfile)
+        if glob.glob(pathname):
+            self._extract()
+            return
+
+        # Check if the user requested to download the dataset
+        if not self.download:
+            raise RuntimeError(
+                f"Dataset not found in `root={self.root}` and `download=False`, "
+                "either specify a different `root` directory or use `download=True` "
+                "to automaticaly download the dataset."
+            )
+
+        # Download the dataset
+        self._download()
+        self._extract()
+
+    def _download(self) -> None:
+        """Download the dataset."""
+        download_url(self.url, self.root, filename=self.zipfile, md5=self.md5)
+
+    def _extract(self) -> None:
+        """Extract the dataset."""
+        extract_archive(os.path.join(self.root, self.zipfile))