GlobalBuildingMap: add new dataset

docs/api/datasets.rst

@@ -56,6 +56,11 @@ Chesapeake Land Cover
 .. autoclass:: ChesapeakeWV
 .. autoclass:: ChesapeakeCVPR
 
+GlobalBuildingMap
+^^^^^^^^^^^^^^^^^
+
+.. autoclass:: GlobalBuildingMap
+
 Global Mangrove Distribution
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

docs/api/datasets/geo_datasets.csv

@@ -5,6 +5,7 @@ Dataset,Type,Source,License,Size (px),Resolution (m)
 `Aster Global DEM`_,DEM,Aster,"public domain","3,601x3,601",30
 `Canadian Building Footprints`_,Geometries,Bing Imagery,"ODbL-1.0",-,-
 `Chesapeake Land Cover`_,"Imagery, Masks",NAIP,"CC0-1.0",-,1
+`GlobalBuildingMap`_,Masks,PlanetScope,CC-BY-4.0,180K,3
 `Global Mangrove Distribution`_,Masks,"Remote Sensing, In Situ Measurements","public domain",-,3
 `Cropland Data Layer`_,Masks,Landsat,"public domain",-,30
 `EDDMapS`_,Points,Citizen Scientists,-,-,-

tests/data/gbm/data.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import numpy as np
+import rasterio
+from rasterio import Affine
+from rasterio.crs import CRS
+
+SIZE = 36
+
+np.random.seed(0)
+
+profile = {
+    'driver': 'GTiff',
+    'dtype': 'uint8',
+    'width': SIZE,
+    'height': SIZE,
+    'count': 1,
+    'crs': CRS.from_epsg(3857),
+    'transform': Affine(3.0, 0.0, -1333.7802539161332, 0.0, -3.0, 1120234.423089223),
+}
+
+Z = np.random.choice([0, 255], size=(SIZE, SIZE))
+
+with rasterio.open('GBM_v1_e000_n10_e005_n05.tif', 'w', **profile) as src:
+    src.write(Z, 1)

tests/datasets/test_gbm.py

@@ -0,0 +1,59 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pytest
+import torch
+from rasterio.crs import CRS
+
+from torchgeo.datasets import (
+    BoundingBox,
+    DatasetNotFoundError,
+    GlobalBuildingMap,
+    IntersectionDataset,
+    UnionDataset,
+)
+
+
+class TestGlobalBuildingMap:
+    @pytest.fixture
+    def dataset(self) -> GlobalBuildingMap:
+        paths = os.path.join('tests', 'data', 'gbm')
+        return GlobalBuildingMap(paths)
+
+    def test_getitem(self, dataset: GlobalBuildingMap) -> None:
+        x = dataset[dataset.bounds]
+        assert isinstance(x, dict)
+        assert isinstance(x['crs'], CRS)
+        assert isinstance(x['mask'], torch.Tensor)
+
+    def test_len(self, dataset: GlobalBuildingMap) -> None:
+        assert len(dataset) == 1
+
+    def test_and(self, dataset: GlobalBuildingMap) -> None:
+        ds = dataset & dataset
+        assert isinstance(ds, IntersectionDataset)
+
+    def test_or(self, dataset: GlobalBuildingMap) -> None:
+        ds = dataset | dataset
+        assert isinstance(ds, UnionDataset)
+
+    def test_plot(self, dataset: GlobalBuildingMap) -> None:
+        sample = dataset[dataset.bounds]
+        sample['prediction'] = sample['mask']
+        dataset.plot(sample, suptitle='Test')
+        plt.close()
+
+    def test_no_data(self, tmp_path: Path) -> None:
+        with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
+            GlobalBuildingMap(tmp_path)
+
+    def test_invalid_query(self, dataset: GlobalBuildingMap) -> 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

@@ -49,6 +49,7 @@
 from .forestdamage import ForestDamage
 from .ftw import FieldsOfTheWorld
 from .gbif import GBIF
+from .gbm import GlobalBuildingMap
 from .geo import (
     GeoDataset,
     IntersectionDataset,
@@ -216,6 +217,7 @@
     'GeoDataset',
     'GeoNRW',
     'GlobBiomass',
+    'GlobalBuildingMap',
     'HySpecNet11k',
     'IDTReeS',
     'INaturalist',

torchgeo/datasets/gbm.py

@@ -0,0 +1,69 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""GlobalBuildingMap."""
+
+from typing import Any
+
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+
+from .geo import RasterDataset
+
+
+class GlobalBuildingMap(RasterDataset):
+    """GlobalBuildingMap dataset.
+
+    The GlobalBuildingMap (GBM) dataset provides the highest resolution and highest
+    accuracy building footprint map on a global scale ever created. GBM was generated
+    by training and applying modern deep neural networks on nearly 800,000 satellite
+    images. The dataset is stored in 5 by 5 degree tiles in geotiff format.
+
+    The GlobalBuildingMap is generated by applying an ensemble of deep neural networks
+    on nearly 800,000 satellite images of about 3m resolution. The deep neural networks
+    were trained with manually inspected training samples generated from OpenStreetMap.
+
+    If you use this dataset in your research, please cite the following paper:
+
+    * https://arxiv.org/abs/2404.13911
+
+    .. versionadded:: 0.7
+    """
+
+    filename_glob = 'GBM_v1_*'
+    is_image = False
+
+    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.
+        """
+        ncols = 2 if 'prediction' in sample else 1
+        fig, axs = plt.subplots(ncols=ncols, squeeze=False)
+
+        axs[0, 0].imshow(sample['mask'])
+        axs[0, 0].axis('off')
+        if show_titles:
+            axs[0, 0].set_title('Mask')
+
+        if 'prediction' in sample:
+            axs[0, 1].imshow(sample['prediction'])
+            axs[0, 1].axis('off')
+            if show_titles:
+                axs[0, 1].set_title('Prediction')
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+
+        return fig