mypy and docs

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`_,CD,"MAXAR, NAIP, Capella, Umbra","CC-BY-SA-4.0",3239,4,"0.1-1","RGB,SAR"
+`BRIGHTDFC2025`_,CD,"MAXAR, NAIP, Capella, Umbra","CC-BY-SA-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

torchgeo/datasets/bright.py

@@ -20,7 +20,7 @@
 
 from .errors import DatasetNotFoundError
 from .geo import NonGeoDataset
-from .utils import Path, download_url, extract_archive
+from .utils import Path, download_url, extract_archive, check_integrity
 
 
 class BRIGHTDFC2025(NonGeoDataset):
@@ -147,8 +147,12 @@ def __getitem__(self, index: int) -> dict[str, Tensor]:
 
         return sample
 
-    def _get_paths(self) -> tuple[list[Path], list[Path], list[Path]]:
-        """Get paths to the dataset files based on specified splits."""
+    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
+        """
         split_file = self.split_files[self.split]
 
         file_path = os.path.join(self.root, self.data_dir, split_file)
@@ -212,7 +216,7 @@ def _verify(self) -> None:
         exists = []
         zip_file_path = os.path.join(self.root, self.data_dir + '.zip')
         if os.path.exists(zip_file_path):
-            if self.checksum and not check_integrity(zip_file_path, split_info['md5']):
+            if self.checksum and not check_integrity(zip_file_path, self.md5):
                 raise RuntimeError('Dataset found, but corrupted.')
             exists.append(True)
             extract_archive(zip_file_path, self.root)
@@ -257,7 +261,7 @@ def _load_image(self, path: Path) -> Tensor:
         """
         with rasterio.open(path) as src:
             img = src.read()
-            tensor = torch.from_numpy(img).float()
+            tensor: Tensor = torch.from_numpy(img).float()
         return tensor
 
     def plot(