Adds the LandCoverAI100 dataset and datamodule for use in semantic segmentation notebooks (#2262)

* Add dataset and datamodule

* Add docs

* Tests

* Ran ruff one time

* Fixture needs a params kwarg

* Make dataset work

* Add versionadded to datamodule

* Add conf file to test new datamodule

* Test datamodule

* Changing dataset URL

* Update main hash

docs/api/datamodules.rst

@@ -123,6 +123,7 @@ LandCover.ai
 ^^^^^^^^^^^^
 
 .. autoclass:: LandCoverAIDataModule
+.. autoclass:: LandCoverAI100DataModule
 
 LEVIR-CD
 ^^^^^^^^

docs/api/datasets.rst

@@ -316,6 +316,7 @@ LandCover.ai
 ^^^^^^^^^^^^
 
 .. autoclass:: LandCoverAI
+.. autoclass:: LandCoverAI100
 
 LEVIR-CD
 ^^^^^^^^

tests/conf/landcoverai100.yaml

@@ -0,0 +1,16 @@
+model:
+  class_path: SemanticSegmentationTask
+  init_args:
+    loss: "ce"
+    model: "unet"
+    backbone: "resnet18"
+    in_channels: 3
+    num_classes: 5
+    num_filters: 1
+    ignore_index: null
+data:
+  class_path: LandCoverAI100DataModule
+  init_args:
+    batch_size: 1
+  dict_kwargs:
+    root: "tests/data/landcoverai"

tests/datasets/test_landcoverai.py

@@ -3,6 +3,7 @@
 
 import os
 import shutil
+from itertools import product
 from pathlib import Path
 
 import matplotlib.pyplot as plt
@@ -17,6 +18,7 @@ from torchgeo.datasets import (
     BoundingBox,
     DatasetNotFoundError,
     LandCoverAI,
+    LandCoverAI100,
     LandCoverAIGeo,
 )
 
@@ -72,20 +74,25 @@ class TestLandCoverAIGeo:
 class TestLandCoverAI:
     pytest.importorskip('cv2', minversion='4.5.4')
 
-    @pytest.fixture(params=['train', 'val', 'test'])
+    @pytest.fixture(
+        params=product([LandCoverAI100, LandCoverAI], ['train', 'val', 'test'])
+    )
     def dataset(
         self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
     ) -> LandCoverAI:
+        base_class: type[LandCoverAI] = request.param[0]
+        split: str = request.param[1]
         md5 = 'ff8998857cc8511f644d3f7d0f3688d0'
-        monkeypatch.setattr(LandCoverAI, 'md5', md5)
+        monkeypatch.setattr(base_class, 'md5', md5)
         url = os.path.join('tests', 'data', 'landcoverai', 'landcover.ai.v1.zip')
-        monkeypatch.setattr(LandCoverAI, 'url', url)
+        monkeypatch.setattr(base_class, 'url', url)
         sha256 = 'ecec8e871faf1bbd8ca525ca95ddc1c1f5213f40afb94599884bd85f990ebd6b'
-        monkeypatch.setattr(LandCoverAI, 'sha256', sha256)
+        monkeypatch.setattr(base_class, 'sha256', sha256)
+        if base_class == LandCoverAI100:
+            monkeypatch.setattr(base_class, 'filename', 'landcover.ai.v1.zip')
         root = tmp_path
-        split = request.param
         transforms = nn.Identity()
-        return LandCoverAI(root, split, transforms, download=True, checksum=True)
+        return base_class(root, split, transforms, download=True, checksum=True)
 
     def test_getitem(self, dataset: LandCoverAI) -> None:
         x = dataset[0]

tests/trainers/test_segmentation.py

@@ -62,6 +62,7 @@ class TestSemanticSegmentationTask:
             'l7irish',
             'l8biome',
             'landcoverai',
+            'landcoverai100',
             'loveda',
             'naipchesapeake',
             'potsdam2d',

torchgeo/datamodules/__init__.py

@@ -23,7 +23,7 @@ from .inria import InriaAerialImageLabelingDataModule
 from .iobench import IOBenchDataModule
 from .l7irish import L7IrishDataModule
 from .l8biome import L8BiomeDataModule
-from .landcoverai import LandCoverAIDataModule
+from .landcoverai import LandCoverAI100DataModule, LandCoverAIDataModule
 from .levircd import LEVIRCDDataModule, LEVIRCDPlusDataModule
 from .loveda import LoveDADataModule
 from .naip import NAIPChesapeakeDataModule
@@ -82,6 +82,7 @@ __all__ = (
     'GID15DataModule',
     'InriaAerialImageLabelingDataModule',
     'LandCoverAIDataModule',
+    'LandCoverAI100DataModule',
     'LEVIRCDDataModule',
     'LEVIRCDPlusDataModule',
     'LoveDADataModule',

torchgeo/datamodules/landcoverai.py

@@ -1,13 +1,13 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-"""LandCover.ai datamodule."""
+"""LandCover.ai datamodules."""
 
 from typing import Any
 
 import kornia.augmentation as K
 
-from ..datasets import LandCoverAI
+from ..datasets import LandCoverAI, LandCoverAI100
 from ..transforms import AugmentationSequential
 from .geo import NonGeoDataModule
 
@@ -43,3 +43,29 @@ class LandCoverAIDataModule(NonGeoDataModule):
         self.aug = AugmentationSequential(
             K.Normalize(mean=self.mean, std=self.std), data_keys=['image', 'mask']
         )
+
+
+class LandCoverAI100DataModule(NonGeoDataModule):
+    """LightningDataModule implementation for the LandCoverAI100 dataset.
+
+    Uses the train/val/test splits from the dataset.
+
+    .. versionadded:: 0.7
+    """
+
+    def __init__(
+        self, batch_size: int = 64, num_workers: int = 0, **kwargs: Any
+    ) -> None:
+        """Initialize a new LandCoverAI100DataModule instance.
+
+        Args:
+            batch_size: Size of each mini-batch.
+            num_workers: Number of workers for parallel data loading.
+            **kwargs: Additional keyword arguments passed to
+                :class:`~torchgeo.datasets.LandCoverAI100`.
+        """
+        super().__init__(LandCoverAI100, batch_size, num_workers, **kwargs)
+
+        self.aug = AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std), data_keys=['image', 'mask']
+        )

torchgeo/datasets/__init__.py

@@ -65,7 +65,7 @@ from .inria import InriaAerialImageLabeling
 from .iobench import IOBench
 from .l7irish import L7Irish
 from .l8biome import L8Biome
-from .landcoverai import LandCoverAI, LandCoverAIBase, LandCoverAIGeo
+from .landcoverai import LandCoverAI, LandCoverAI100, LandCoverAIBase, LandCoverAIGeo
 from .landsat import (
     Landsat,
     Landsat1,
@@ -224,6 +224,7 @@ __all__ = (
     'IDTReeS',
     'InriaAerialImageLabeling',
     'LandCoverAI',
+    'LandCoverAI100',
     'LEVIRCD',
     'LEVIRCDBase',
     'LEVIRCDPlus',

torchgeo/datasets/landcoverai.py

@@ -401,10 +401,26 @@ class LandCoverAI(LandCoverAIBase, NonGeoDataset):
         super()._extract()
 
         # Generate train/val/test splits
-        # Always check the sha256 of this file before executing
-        # to avoid malicious code injection
-        with working_dir(self.root):
-            with open('split.py') as f:
-                split = f.read().encode('utf-8')
-                assert hashlib.sha256(split).hexdigest() == self.sha256
-                exec(split)
+        # Always check the sha256 of this file before executing to avoid malicious code injection
+        # The LandCoverAI100 dataset doesn't contain split.py, so only run if split.py exists
+        if os.path.exists(os.path.join(self.root, 'split.py')):
+            with working_dir(self.root):
+                with open('split.py') as f:
+                    split = f.read().encode('utf-8')
+                    assert hashlib.sha256(split).hexdigest() == self.sha256
+                    exec(split)
+
+
+class LandCoverAI100(LandCoverAI):
+    """Subset of LandCoverAI containing only 100 images.
+
+    Intended for tutorials and demonstrations, not for benchmarking.
+
+    Maintains the same file structure, classes, and train-val-test split.
+
+    .. versionadded:: 0.7
+    """
+
+    url = 'https://huggingface.co/datasets/torchgeo/landcoverai/resolve/5cdf9299bd6c1232506cf79373df01f6e6596b50/landcoverai100.zip'
+    filename = 'landcoverai100.zip'
+    md5 = '66eb33b5a0cabb631836ce0a4eafb7cd'