Add EUDEM dataset

docs/api/datasets.rst

@@ -16,7 +16,7 @@ Aster Global Digital Evaluation Model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autoclass:: AsterGDEM
-    
+
 Canadian Building Footprints
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -57,8 +57,14 @@ Esri2020
 
 .. autoclass:: Esri2020
 
+EU-DEM
+^^^^^^
+
+.. autoclass:: EUDEM
+
 GlobBiomass
 ^^^^^^^^^^^
+
 .. autoclass:: GlobBiomass
 
 Landsat

tests/data/eudem/data.py

@@ -0,0 +1,64 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import hashlib
+import os
+import random
+import zipfile
+
+import numpy as np
+import rasterio
+
+np.random.seed(0)
+random.seed(0)
+
+SIZE = 64
+
+files = [{"image": "eu_dem_v11_E30N10.TIF"}, {"image": "eu_dem_v11_E30N10.TIF.ovr"}]
+
+
+def create_file(path: str, dtype: str, num_channels: int) -> None:
+    profile = {}
+    profile["driver"] = "GTiff"
+    profile["dtype"] = dtype
+    profile["count"] = num_channels
+    profile["crs"] = "epsg:4326"
+    profile["transform"] = rasterio.transform.from_bounds(0, 0, 1, 1, 1, 1)
+    profile["height"] = SIZE
+    profile["width"] = SIZE
+    profile["compress"] = "lzw"
+    profile["predictor"] = 2
+
+    Z = np.random.randint(
+        np.iinfo(profile["dtype"]).max, size=(1, SIZE, SIZE), dtype=profile["dtype"]
+    )
+    src = rasterio.open(path, "w", **profile)
+    src.write(Z)
+
+
+if __name__ == "__main__":
+    zipfilename = "eu_dem_v11_E30N10.zip"
+    files_to_zip = []
+
+    for file_dict in files:
+        path = file_dict["image"]
+        # remove old data
+        if os.path.exists(path):
+            os.remove(path)
+        # Create mask file
+        create_file(path, dtype="int32", num_channels=1)
+        files_to_zip.append(path)
+
+    # Compress data
+    with zipfile.ZipFile(zipfilename, "w") as zip:
+        for file in files_to_zip:
+            zip.write(file, arcname=file)
+
+    # Compute checksums
+    with open(zipfilename, "rb") as f:
+        md5 = hashlib.md5(f.read()).hexdigest()
+        print(f"{zipfilename}: {md5}")
+
+    # remove TIF files
+    for file_dict in files:
+        os.remove(file_dict["image"])

tests/datasets/test_eudem.py

@@ -0,0 +1,79 @@
+# 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
+
+from torchgeo.datasets import EUDEM, BoundingBox, IntersectionDataset, UnionDataset
+
+
+class TestEUDEM:
+    @pytest.fixture
+    def dataset(
+        self, monkeypatch: Generator[MonkeyPatch, None, None], tmp_path: Path
+    ) -> EUDEM:
+
+        md5s = {"eu_dem_v11_E30N10.zip": "ef148466c02197a08be169eaad186591"}
+        monkeypatch.setattr(EUDEM, "md5s", md5s)  # type: ignore[attr-defined]
+        zipfile = os.path.join("tests", "data", "eudem", "eu_dem_v11_E30N10.zip")
+        shutil.copy(zipfile, tmp_path)
+        root = str(tmp_path)
+        transforms = nn.Identity()  # type: ignore[attr-defined]
+        return EUDEM(root, transforms=transforms)
+
+    def test_getitem(self, dataset: EUDEM) -> None:
+        x = dataset[dataset.bounds]
+        assert isinstance(x, dict)
+        assert isinstance(x["crs"], CRS)
+        assert isinstance(x["mask"], torch.Tensor)
+
+    def test_extracted_already(self, dataset: EUDEM) -> None:
+        zipfile = os.path.join(dataset.root, "eu_dem_v11_E30N10.zip")
+        shutil.unpack_archive(zipfile, dataset.root, "zip")
+        EUDEM(dataset.root)
+
+    def test_no_dataset(self, tmp_path: Path) -> None:
+        shutil.rmtree(tmp_path)
+        os.makedirs(tmp_path)
+        with pytest.raises(RuntimeError, match="Dataset not found in"):
+            EUDEM(root=str(tmp_path))
+
+    def test_corrupted(self, tmp_path: Path) -> None:
+        with open(os.path.join(tmp_path, "eu_dem_v11_E30N10.zip"), "w") as f:
+            f.write("bad")
+        with pytest.raises(RuntimeError, match="Dataset found, but corrupted."):
+            EUDEM(root=str(tmp_path), checksum=True)
+
+    def test_and(self, dataset: EUDEM) -> None:
+        ds = dataset & dataset
+        assert isinstance(ds, IntersectionDataset)
+
+    def test_or(self, dataset: EUDEM) -> None:
+        ds = dataset | dataset
+        assert isinstance(ds, UnionDataset)
+
+    def test_plot(self, dataset: EUDEM) -> None:
+        query = dataset.bounds
+        x = dataset[query]
+        dataset.plot(x, suptitle="Test")
+
+    def test_plot_prediction(self, dataset: EUDEM) -> None:
+        query = dataset.bounds
+        x = dataset[query]
+        x["prediction"] = x["mask"].clone()
+        dataset.plot(x, suptitle="Prediction")
+
+    def test_invalid_query(self, dataset: EUDEM) -> None:
+        query = BoundingBox(100, 100, 100, 100, 0, 0)
+        with pytest.raises(
+            IndexError, match="query: .* not found in index with bounds:"
+        ):
+            dataset[query]

torchgeo/datasets/__init__.py

@@ -30,6 +30,7 @@
 from .enviroatlas import EnviroAtlas
 from .esri2020 import Esri2020
 from .etci2021 import ETCI2021
+from .eudem import EUDEM
 from .eurosat import EuroSAT
 from .fair1m import FAIR1M
 from .geo import (
@@ -103,6 +104,7 @@
     "ChesapeakeCVPR",
     "CMSGlobalMangroveCanopy",
     "Esri2020",
+    "EUDEM",
     "GlobBiomass",
     "Landsat",
     "Landsat1",

torchgeo/datasets/eudem.py

@@ -0,0 +1,185 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""European Digital Elevation Model (EU-DEM) dataset."""
+
+import glob
+import os
+from typing import Any, Callable, Dict, Optional
+
+import matplotlib.pyplot as plt
+from rasterio.crs import CRS
+
+from .geo import RasterDataset
+from .utils import check_integrity, extract_archive
+
+
+class EUDEM(RasterDataset):
+    """European Digital Elevation Model (EU-DEM) Dataset.
+
+    The `EU-DEM
+    <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1?tab=mapview>`_
+    dataset is a Digital Elevation Model of reference for the entire European region.
+    The dataset can be downloaded from this `website
+    <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1?tab=mapview>`_
+    after making an account. A dataset factsheet is available
+    `here <https://land.copernicus.eu/user-corner/publications/eu-dem-flyer/view>`__.
+
+    Dataset features:
+
+    * DEMs at 25 m per pixel spatial resolution (~40,000x40,0000 px)
+    * vertical accuracy of +/- 7 m RMSE
+    * data fused from `ASTER GDEM
+      <https://lpdaac.usgs.gov/news/nasa-and-meti-release-aster-global-dem-version-3/>`_,
+      `SRTM <https://www2.jpl.nasa.gov/srtm/>`_ and Russian topomaps
+
+    Dataset format:
+
+    * DEMs are single-channel tif files
+
+    If you use this dataset in your research, please give credit to:
+
+    * `Copernicus <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1>`_
+
+    .. versionadded:: 0.3
+    """
+
+    is_image = False
+    filename_glob = "eu_dem_v11_*.TIF"
+    zipfile_glob = "eu_dem_v11_*[A-Z0-9].zip"
+    filename_regex = "(?P<name>[eudem_v11]{10})_(?P<id>[A-Z0-9]{6})"
+
+    md5s = {
+        "eu_dem_v11_E00N20.zip": "96edc7e11bc299b994e848050d6be591",
+        "eu_dem_v11_E10N00.zip": "e14be147ac83eddf655f4833d55c1571",
+        "eu_dem_v11_E10N10.zip": "2eb5187e4d827245b33768404529c709",
+        "eu_dem_v11_E10N20.zip": "1afc162eb131841aed0d00b692b870a8",
+        "eu_dem_v11_E20N10.zip": "77b040791b9fb7de271b3f47130b4e0c",
+        "eu_dem_v11_E20N20.zip": "89b965abdcb1dbd479c61117f55230c8",
+        "eu_dem_v11_E20N30.zip": "f5cb1b05813ae8ffc9e70f0ad56cc372",
+        "eu_dem_v11_E20N40.zip": "81be551ff646802d7d820385de7476e9",
+        "eu_dem_v11_E20N50.zip": "bbc351713ea3eb7e9eb6794acb9e4bc8",
+        "eu_dem_v11_E30N10.zip": "68fb95aac33a025c4f35571f32f237ff",
+        "eu_dem_v11_E30N20.zip": "da8ad029f9cc1ec9234ea3e7629fe18d",
+        "eu_dem_v11_E30N30.zip": "de27c78d0176e45aec5c9e462a95749c",
+        "eu_dem_v11_E30N40.zip": "4c00e58b624adfc4a5748c922e77ee40",
+        "eu_dem_v11_E30N50.zip": "4a21a88f4d2047b8995d1101df0b3a77",
+        "eu_dem_v11_E40N10.zip": "32fdf4572581eddc305a21c5d2f4bc81",
+        "eu_dem_v11_E40N20.zip": "71b027f29258493dd751cfd63f08578f",
+        "eu_dem_v11_E40N30.zip": "c6c21289882c1f74fc4649d255302c64",
+        "eu_dem_v11_E40N40.zip": "9f26e6e47f4160ef8ea5200e8cf90a45",
+        "eu_dem_v11_E40N50.zip": "a8c3c1c026cdd1537b8a3822c15834d9",
+        "eu_dem_v11_E50N10.zip": "9584273c7708b8e935f2bac3e30c19c6",
+        "eu_dem_v11_E50N20.zip": "8efdea43e7b6819861935d5a768a55f2",
+        "eu_dem_v11_E50N30.zip": "e39e58df1c13ac35eb0b29fb651f313c",
+        "eu_dem_v11_E50N40.zip": "d84395ab52ad254d930db17398fffc50",
+        "eu_dem_v11_E50N50.zip": "6abe852f4a20962db0e355ffc0d695a4",
+        "eu_dem_v11_E60N10.zip": "b6a3b8a39a4efc01c7e2cd8418672559",
+        "eu_dem_v11_E60N20.zip": "71dc3c55ab5c90628ce2149dbd60f090",
+        "eu_dem_v11_E70N20.zip": "5342465ad60cf7d28a586c9585179c35",
+    }
+
+    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,
+        checksum: bool = False,
+    ) -> None:
+        """Initialize a new Dataset instance.
+
+        Args:
+            root: root directory where dataset can be found, here the collection of
+                individual zip files for each tile should 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
+            checksum: if True, check the MD5 of the downloaded files (may be slow)
+
+        Raises:
+            FileNotFoundError: if no files are found in ``root``
+        """
+        self.root = root
+        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 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_glob)
+        if glob.glob(pathname):
+            for zipfile in glob.iglob(pathname):
+                filename = os.path.basename(zipfile)
+                if self.checksum and not check_integrity(zipfile, self.md5s[filename]):
+                    raise RuntimeError("Dataset found, but corrupted.")
+                extract_archive(zipfile)
+            return
+
+        raise RuntimeError(
+            f"Dataset not found in `root={self.root}` "
+            "either specify a different `root` directory or make sure you "
+            "have manually downloaded the dataset as suggested in the documentation."
+        )
+
+    def plot(  # type: ignore[override]
+        self,
+        sample: Dict[str, Any],
+        show_titles: bool = True,
+        suptitle: Optional[str] = None,
+    ) -> plt.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))
+
+        if showing_predictions:
+            axs[0].imshow(mask)
+            axs[0].axis("off")
+            axs[1].imshow(pred)
+            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