request

docs/api/datasets.rst

@@ -217,8 +217,8 @@ BioMassters
 
 .. autoclass:: BioMassters
 
-BRIGHT DFC2025
-^^^^^^^^^^^^^^
+BRIGHT
+^^^^^^
 
 .. autoclass:: BRIGHTDFC2025
 

docs/api/datasets/non_geo_datasets.csv

@@ -3,7 +3,7 @@ Dataset,Task,Source,License,# Samples,# Classes,Size (px),Resolution (m),Bands
 `Benin Cashew Plantations`_,S,Airbus Pléiades,"CC-BY-4.0",70,6,"1,122x1,186",10,MSI
 `BigEarthNet`_,C,Sentinel-1/2,"CDLA-Permissive-1.0","590,326",19--43,120x120,10,"SAR, MSI"
 `BioMassters`_,R,Sentinel-1/2 and Lidar,"CC-BY-4.0",,,256x256, 10, "SAR, MSI"
-`BRIGHT DFC2025`_,CD,"MAXAR, NAIP, Capella, Umbra","CC-BY-SA-4.0",3239,4,"0.1-1","RGB,SAR"
+`BRIGHT`_,CD,"MAXAR, NAIP, Capella, Umbra","CC-BY-NC-4.0",3239,4,"0.1-1","RGB,SAR"
 `CaBuAr`_,CD,Sentinel-2,"OpenRAIL",424,2,512x512,20,MSI
 `CaFFe`_,S,"Sentinel-1, TerraSAR-X, TanDEM-X, ENVISAT, ERS-1/2, ALOS PALSAR, and RADARSAT-1","CC-BY-4.0","19092","2 or 4","512x512",6-20,"SAR"
 `ChaBuD`_,CD,Sentinel-2,"OpenRAIL",356,2,512x512,10,MSI

tests/datasets/test_bright.py

@@ -32,14 +32,14 @@ def dataset(
     def test_getitem(self, dataset: BRIGHTDFC2025) -> None:
         x = dataset[0]
         assert isinstance(x, dict)
-        assert isinstance(x['pre_image'], torch.Tensor)
-        assert x['pre_image'].shape[0] == 3
-        assert isinstance(x['post_image'], torch.Tensor)
-        assert x['post_image'].shape[0] == 3
-        assert x['pre_image'].shape[-2:] == x['post_image'].shape[-2:]
+        assert isinstance(x['image_pre'], torch.Tensor)
+        assert x['image_pre'].shape[0] == 3
+        assert isinstance(x['image_post'], torch.Tensor)
+        assert x['image_post'].shape[0] == 3
+        assert x['image_pre'].shape[-2:] == x['image_post'].shape[-2:]
         if dataset.split != 'test':
             assert isinstance(x['mask'], torch.Tensor)
-            assert x['pre_image'].shape[-2:] == x['mask'].shape[-2:]
+            assert x['image_pre'].shape[-2:] == x['mask'].shape[-2:]
 
     def test_len(self, dataset: BRIGHTDFC2025) -> None:
         if dataset.split == 'train':

torchgeo/datasets/bright.py

@@ -26,7 +26,8 @@
 class BRIGHTDFC2025(NonGeoDataset):
     """BRIGHT DFC2025 dataset.
 
-    The `BRIGHT <https://github.com/ChenHongruixuan/BRIGHT>`__ dataset consists of bi-temporal high-resolution multimodal images for
+    The `BRIGHT <https://github.com/ChenHongruixuan/BRIGHT>`__ dataset consists of bi-temporal
+    high-resolution multimodal images for
     building damage assessment. The dataset is part of the 2025 IEEE GRSS Data Fusion Contest.
     The pre-disaster images are optical images and the post-disaster images are SAR images, and
     targets were manually annotated. The dataset is split into train, val, and test splits, but
@@ -64,7 +65,7 @@ class BRIGHTDFC2025(NonGeoDataset):
 
     md5 = '2c435bb50345d425390eff59a92134ac'
 
-    url = 'https://huggingface.co/datasets/Kullervo/BRIGHT/resolve/50901f05db4acbd141e7c96d719d8317910498fb/dfc25_track2_trainval.zip'
+    url = 'https://huggingface.co/datasets/torchgeo/bright/resolve/d19972f5e682ad684dcde35529a6afad4c719f1b/dfc25_track2_trainval_with_split.zip'
 
     data_dir = 'dfc25_track2_trainval'
 
@@ -126,17 +127,18 @@ def __getitem__(self, index: int) -> dict[str, Tensor]:
             index: index to return
 
         Returns:
-            data and target at that index
+            data and target at that index, pre and post image
+            are returned under separate image keys
         """
         idx_paths = self.sample_paths[index]
 
-        pre_image = self._load_image(idx_paths['pre_image']).float()
-        post_image = self._load_image(idx_paths['post_image']).float()
+        image_pre = self._load_image(idx_paths['image_pre']).float()
+        image_post = self._load_image(idx_paths['image_post']).float()
         # https://github.com/ChenHongruixuan/BRIGHT/blob/11b1ffafa4d30d2df2081189b56864b0de4e3ed7/dfc25_benchmark/dataset/make_data_loader.py#L101
         # post image is stacked to also have 3 channels
-        post_image = repeat(post_image, 'c h w -> (repeat c) h w', repeat=3)
+        image_post = repeat(image_post, 'c h w -> (repeat c) h w', repeat=3)
 
-        sample = {'pre_image': pre_image, 'post_image': post_image}
+        sample = {'image_pre': image_pre, 'image_post': image_post}
 
         if 'target' in idx_paths and self.split != 'test':
             target = self._load_image(idx_paths['target']).long()
@@ -151,7 +153,7 @@ def _get_paths(self) -> list[dict[str, str]]:
         """Get paths to the dataset files based on specified splits.
 
         Returns:
-            a list of dictionaries containing paths to the pre,post and target images
+            a list of dictionaries containing paths to the pre, post, and target images
         """
         split_file = self.split_files[self.split]
 
@@ -166,14 +168,14 @@ def _get_paths(self) -> list[dict[str, str]]:
 
         sample_paths = [
             {
-                'pre_image': os.path.join(
+                'image_pre': os.path.join(
                     self.root,
                     self.data_dir,
                     dir_split_name,
                     'pre-event',
                     f'{sample_id.strip()}_pre_disaster.tif',
                 ),
-                'post_image': os.path.join(
+                'image_post': os.path.join(
                     self.root,
                     self.data_dir,
                     dir_split_name,
@@ -290,10 +292,10 @@ def plot(
 
         fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(15, 5))
 
-        axs[0].imshow(sample['pre_image'].permute(1, 2, 0) / 255.0)
+        axs[0].imshow(sample['image_pre'].permute(1, 2, 0) / 255.0)
         axs[0].axis('off')
 
-        axs[1].imshow(sample['post_image'].permute(1, 2, 0) / 255.0)
+        axs[1].imshow(sample['image_post'].permute(1, 2, 0) / 255.0)
         axs[1].axis('off')
 
         cmap = colors.ListedColormap(self.colormap)