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BigEarthNet.py

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+import os
+import json
+import shutil
+import string
+import tifffile
+import datasets
+
+import numpy as np
+import pandas as pd
+
+class_sets = {
+        19: [
+            'Urban fabric',
+            'Industrial or commercial units',
+            'Arable land',
+            'Permanent crops',
+            'Pastures',
+            'Complex cultivation patterns',
+            'Land principally occupied by agriculture, with significant areas of'
+            ' natural vegetation',
+            'Agro-forestry areas',
+            'Broad-leaved forest',
+            'Coniferous forest',
+            'Mixed forest',
+            'Natural grassland and sparsely vegetated areas',
+            'Moors, heathland and sclerophyllous vegetation',
+            'Transitional woodland, shrub',
+            'Beaches, dunes, sands',
+            'Inland wetlands',
+            'Coastal wetlands',
+            'Inland waters',
+            'Marine waters',
+        ],
+        43: [
+            'Continuous urban fabric',
+            'Discontinuous urban fabric',
+            'Industrial or commercial units',
+            'Road and rail networks and associated land',
+            'Port areas',
+            'Airports',
+            'Mineral extraction sites',
+            'Dump sites',
+            'Construction sites',
+            'Green urban areas',
+            'Sport and leisure facilities',
+            'Non-irrigated arable land',
+            'Permanently irrigated land',
+            'Rice fields',
+            'Vineyards',
+            'Fruit trees and berry plantations',
+            'Olive groves',
+            'Pastures',
+            'Annual crops associated with permanent crops',
+            'Complex cultivation patterns',
+            'Land principally occupied by agriculture, with significant areas of'
+            ' natural vegetation',
+            'Agro-forestry areas',
+            'Broad-leaved forest',
+            'Coniferous forest',
+            'Mixed forest',
+            'Natural grassland',
+            'Moors and heathland',
+            'Sclerophyllous vegetation',
+            'Transitional woodland/shrub',
+            'Beaches, dunes, sands',
+            'Bare rock',
+            'Sparsely vegetated areas',
+            'Burnt areas',
+            'Inland marshes',
+            'Peatbogs',
+            'Salt marshes',
+            'Salines',
+            'Intertidal flats',
+            'Water courses',
+            'Water bodies',
+            'Coastal lagoons',
+            'Estuaries',
+            'Sea and ocean',
+        ],
+    }
+
+label_converter = {
+        0: 0,
+        1: 0,
+        2: 1,
+        11: 2,
+        12: 2,
+        13: 2,
+        14: 3,
+        15: 3,
+        16: 3,
+        18: 3,
+        17: 4,
+        19: 5,
+        20: 6,
+        21: 7,
+        22: 8,
+        23: 9,
+        24: 10,
+        25: 11,
+        31: 11,
+        26: 12,
+        27: 12,
+        28: 13,
+        29: 14,
+        33: 15,
+        34: 15,
+        35: 16,
+        36: 16,
+        38: 17,
+        39: 17,
+        40: 18,
+        41: 18,
+        42: 18,
+    }
+
+S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2517.76053101, 2581.64687018, 2645.51888987, 2368.51236873, 1805.06846033]
+S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1474.78900051, 1439.3086061, 1582.28010962, 1455.52084939, 1343.48379601]
+
+S1_MEAN = [-12.54847273, -20.19237134]
+S1_STD = [5.25697717, 5.91150917]
+
+parts = [f"a{letter}" for letter in string.ascii_lowercase]
+parts.extend([f"b{letter}" for letter in string.ascii_lowercase[:8]]) 
+
+class BigEarthNetDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    DATA_URL = [
+        f"https://huggingface.co/datasets/GFM-Bench/BigEarthNet/resolve/main/data/bigearthnet_part_{part}"
+        for part in parts
+    ]
+
+    metadata = {
+        "s2c": {
+            "bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"],
+            "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1613.7, 2202.4],
+            "mean": S2_MEAN,
+            "std": S2_STD
+        },
+        "s1": {
+            "bands": ["VV", "VH"],
+            "channel_wv": [5500, 5700],
+            "mean": S1_MEAN,
+            "std": S1_STD
+        }
+    }
+
+    SIZE = HEIGHT = WIDTH = 120
+
+    NUM_CLASSES = 19
+
+    spatial_resolution = 10
+
+    def __init__(self, *args, **kwargs):
+        self.class2idx = {c: i for i, c in enumerate(class_sets[43])}
+
+        super().__init__(*args, **kwargs)
+
+    def _info(self):
+        metadata = self.metadata
+        metadata['size'] = self.SIZE
+        metadata['num_classes'] = self.NUM_CLASSES
+        metadata['spatial_resolution'] = self.spatial_resolution
+        return datasets.DatasetInfo(
+            description=json.dumps(metadata),
+            features=datasets.Features({
+                "optical": datasets.Array3D(shape=(12, self.HEIGHT, self.WIDTH), dtype="float32"),
+                "radar": datasets.Array3D(shape=(2, self.HEIGHT, self.WIDTH), dtype="float32"),
+                "optical_channel_wv": datasets.Sequence(datasets.Value("float32")),
+                "radar_channel_wv": datasets.Sequence(datasets.Value("float32")),
+                "label": datasets.Sequence(datasets.Value("float32"), length=self.NUM_CLASSES),
+                "spatial_resolution": datasets.Value("int32"),
+            }),
+        )
+
+    def _split_generators(self, dl_manager):
+        if isinstance(self.DATA_URL, list):
+            try:
+                print("Downloading data files from HF")
+                downloaded_files = dl_manager.download(self.DATA_URL)
+                print("Downloading Finished")
+                combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz")        
+                with open(combined_file, 'wb') as outfile:
+                    counter = 0 
+                    for part_file in downloaded_files:
+                        print(f"copying {counter}-th file")
+                        with open(part_file, 'rb') as infile:
+                            shutil.copyfileobj(infile, outfile)
+                data_dir = dl_manager.extract(combined_file)
+                os.remove(combined_file)
+            except Exception as e:
+                # Print the error message
+                print(f"An error occurred: {e}, setting data_dir to None")
+                data_dir = None
+        else:
+            data_dir = dl_manager.download_and_extract(self.DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "split": 'train',
+                    "data_dir": data_dir, 
+                },
+            ),
+            datasets.SplitGenerator(
+                name="val",
+                gen_kwargs={
+                    "split": 'val',
+                    "data_dir": data_dir,
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test",
+                gen_kwargs={
+                    "split": 'test',
+                    "data_dir": data_dir,
+                },
+            )
+        ]
+
+    def _generate_examples(self, split, data_dir):
+        optical_channel_wv = np.array(self.metadata["s2c"]["channel_wv"])
+        radar_channel_wv = np.array(self.metadata["s1"]["channel_wv"])
+        spatial_resolution = self.spatial_resolution
+
+        data_dir = os.path.join(data_dir, "BigEarthNet")
+        metadata = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
+        metadata = metadata[metadata["split"] == split].reset_index(drop=True)
+
+        for index, row in metadata.iterrows():
+            optical_path = os.path.join(data_dir, row.optical_path)
+            optical = self._read_image(optical_path).astype(np.float32) # CxHxW
+
+            radar_path = os.path.join(data_dir, row.radar_path)
+            radar = self._read_image(radar_path).astype(np.float32)
+
+            label_path = os.path.join(data_dir, row.label_path)
+            label = self._load_label(label_path)
+
+            sample = {
+                "optical": optical,
+                "radar": radar,
+                "optical_channel_wv": optical_channel_wv,
+                "radar_channel_wv": radar_channel_wv,
+                "label": label,
+                "spatial_resolution": spatial_resolution,
+            }
+
+            yield f"{index}", sample
+    
+    def _load_label(self, label_path):
+        with open(label_path) as f:
+            labels = json.load(f)['labels']
+        indices =[self.class2idx[label] for label in labels]
+        indices_optional = [label_converter.get(idx) for idx in indices]
+        indices = [idx for idx in indices_optional if idx is not None]
+        label = np.zeros(19, dtype=np.int64)
+        label[indices] = 1
+        return label
+    
+    def _read_image(self, image_path):
+        """Read tiff image from image_path
+        Args:
+            image_path: 
+                Image path to read from
+
+        Return:
+            image: 
+                C, H, W numpy array image
+        """
+        image = tifffile.imread(image_path)
+        image = np.transpose(image, (2, 0, 1))
+
+        return image