Return timestamp

tests/data/satlas/data.py

@@ -36,6 +36,12 @@
     [7149, 3246, 'S2A_MSIL1C_20220309T032601_N0400_R018_T48RYR_20220309T060235'],
     [1234, 5678, 'S2A_MSIL1C_20220309T032601_N0400_R018_T48RYR_20220309T060235'],
 ]
+times = {
+    '2022-03': '2022-03-01T00:00:00+00:00',
+    'm_3808245_se_17_1_20110801': '2011-08-01T12:00:00+00:00',
+    '2022-01': '2022-01-01T00:00:00+00:00',
+    'S2A_MSIL1C_20220309T032601_N0400_R018_T48RYR_20220309T060235': '2022-03-09T06:02:35+00:00',
+}
 
 FILENAME_HIERARCHY = dict[str, 'FILENAME_HIERARCHY'] | list[str]
 filenames: FILENAME_HIERARCHY = {
@@ -97,6 +103,9 @@ def create_directory(directory: str, hierarchy: FILENAME_HIERARCHY) -> None:
     with open(os.path.join('metadata', 'good_images_lowres_all.json'), 'w') as f:
         json.dump(good_images, f)
 
+    with open(os.path.join('metadata', 'image_times.json'), 'w') as f:
+        json.dump(times, f)
+
     for path in os.listdir('.'):
         if os.path.isdir(path):
             shutil.make_archive(path, 'tar', '.', path)

tests/data/satlas/metadata/image_times.json

@@ -0,0 +1 @@
+{"2022-03": "2022-03-01T00:00:00+00:00", "m_3808245_se_17_1_20110801": "2011-08-01T12:00:00+00:00", "2022-01": "2022-01-01T00:00:00+00:00", "S2A_MSIL1C_20220309T032601_N0400_R018_T48RYR_20220309T060235": "2022-03-09T06:02:35+00:00"}

tests/datasets/test_satlas.py

@@ -37,6 +37,7 @@ def test_getitem(self, dataset: SatlasPretrain, index: int) -> None:
         assert isinstance(x, dict)
         for image in dataset.images:
             assert isinstance(x[f'image_{image}'], Tensor)
+            assert isinstance(x[f'time_{image}'], Tensor)
         for label in dataset.labels:
             assert isinstance(x[f'mask_{label}'], Tensor)
 

torchgeo/datasets/satlas.py

@@ -538,6 +538,7 @@ def __init__(
         root: Path = 'data',
         split: str = 'train_lowres',
         good_images: str = 'good_images_lowres_all',
+        image_times: str = 'image_times',
         images: Iterable[str] = ('sentinel1', 'sentinel2', 'landsat'),
         labels: Iterable[str] = ('land_cover',),
         transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
@@ -550,6 +551,7 @@ def __init__(
             root: Root directory where dataset can be found.
             split: Metadata split to load.
             good_images: Metadata mapping between col/row and directory.
+            image_times: Metadata mapping between directory and ISO time.
             images: List of image products.
             labels: List of label products.
             transforms: A function/transform that takes input sample and its target as
@@ -572,10 +574,17 @@ def __init__(
 
         self._verify()
 
-        self.split = pd.read_json(os.path.join(root, 'metadata', f'{split}.json'))
+        # Read metadata files
+        self.split = pd.read_json(
+            os.path.join(root, 'metadata', f'{split}.json'), typ='frame'
+        )
         self.good_images = pd.read_json(
-            os.path.join(root, 'metadata', f'{good_images}.json')
+            os.path.join(root, 'metadata', f'{good_images}.json'), typ='frame'
+        )
+        self.image_times = pd.read_json(
+            os.path.join(root, 'metadata', f'{image_times}.json'), typ='series'
         )
+
         self.split.columns = ['col', 'row']
         self.good_images.columns = ['col', 'row', 'directory']
         self.good_images = self.good_images.groupby(['col', 'row'])
@@ -646,6 +655,8 @@ def _load_image(
         # Choose a random timestamp
         idx = torch.randint(len(good_directories), (1,))
         directory = good_directories[idx]
+        time = self.image_times[directory].timestamp()
+        sample[f'time_{image}'] = torch.tensor(time)
 
         # Load all bands
         channels = []