import os import json import shutil import datasets import tifffile import pandas as pd import numpy as np from torchgeo.datasets.cdl import CDL from torchgeo.datasets.nlcd import NLCD CMAPS = { 'nlcd': NLCD.cmap, 'cdl': CDL.cmap, } S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2581.64687018, 2368.51236873, 1805.06846033] S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1439.3086061, 1455.52084939, 1343.48379601] subset_names = ["etm_sr_cdl", "etm_sr_nlcd", "etm_toa_cdl", "etm_toa_nlcd", "oli_sr_cdl", "oli_sr_nlcd", "oli_tirs_toa_cdl", "oli_tirs_toa_nlcd", "etm_oli_toa_cdl", "etm_oli_toa_nlcd"] num_classes = { 'etm_sr_cdl': 134, 'etm_sr_nlcd': 21, 'etm_toa_cdl': 134, 'etm_toa_nlcd': 21, 'oli_sr_cdl': 134, 'oli_sr_nlcd': 21, 'oli_tirs_toa_cdl': 134, 'oli_tirs_toa_nlcd': 21, "etm_oli_toa_nlcd": 21, "etm_oli_toa_cdl": 134, } num_channels = { 'etm_sr_cdl': 6, 'etm_sr_nlcd': 6, 'etm_toa_cdl': 9, 'etm_toa_nlcd': 9, 'oli_sr_cdl': 7, 'oli_sr_nlcd': 7, 'oli_tirs_toa_cdl': 11, 'oli_tirs_toa_nlcd': 11, "etm_oli_toa_nlcd": 20, "etm_oli_toa_cdl": 20, } MEAN = [0] STD = [255] metadata = { # TODO: check if info below is correct or not 'etm_sr_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B7"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 2220.0], "mean": MEAN * 6, 'std': STD * 6}, # B6 (Thermal Band) and B8 (Panchromatic Band) are excluded 'etm_sr_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B7"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 2220.0], "mean": MEAN * 6, 'std': STD * 6}, 'etm_toa_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B6L", "B6H", "B7", "B8"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0], "mean": MEAN * 9, 'std': STD * 9}, 'etm_toa_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6L", "B6H", "B7", "B8"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0], "mean": MEAN * 9, 'std': STD * 9}, 'oli_sr_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0], "mean": MEAN * 7, 'std': STD * 7}, 'oli_sr_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0], "mean": MEAN * 7, 'std': STD * 7}, 'oli_tirs_toa_cdl': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 11, 'std': STD * 11}, 'oli_tirs_toa_nlcd': {"bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11"], "channel_wv": [443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 11, 'std': STD * 11}, "etm_oli_toa_nlcd": {"bands":["B1E", "B2E", "B3E", "B4E", "B5E", "B6LE", "B6HE", "B7E", "B8E", "B1O", "B2O", "B3O", "B4O", "B5O", "B6O", "B7O", "B8O", "B9O", "B10O", "B11O"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0, 443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 20, 'std': STD * 20}, "etm_oli_toa_cdl": {"bands":["B1E", "B2E", "B3E", "B4E", "B5E", "B6LE", "B6HE", "B7E", "B8E", "B1O", "B2O", "B3O", "B4O", "B5O", "B6O", "B7O", "B8O", "B9O", "B10O", "B11O"], "channel_wv": [485.0, 560.0, 660.0, 835.0, 1650.0, 10900.0, 10900.0, 2220.0, 710.0, 443.0, 482.0, 562.0, 655.0, 865.0, 1610.0, 2200.0, 590.0, 1735.0, 10800.0, 12000.0], "mean": MEAN * 20, 'std': STD * 20}, } class SSL4EOLBenchmarkDataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") DATA_URL = "https://huggingface.co/datasets/GFM-Bench/SSL4EO-L-Benchmark/resolve/main/SSL4EOLBenchmark.zip" SIZE = HEIGHT = WIDTH = 264 spatial_resolution = 30 BUILDER_CONFIGS = [ datasets.BuilderConfig(name="default"), *[datasets.BuilderConfig(name=name) for name in subset_names] ] DEFAULT_CONFIG_NAME = "oli_tirs_toa_nlcd" def __init__(self, *args, **kwargs): name = kwargs.get('config_name', None) self.NUM_CLASSES = num_classes[name] if name and name != "default" else num_classes['oli_tirs_toa_nlcd'] self.NUM_CHANNELS = num_channels[name] if name and name != "default" else num_channels['oli_tirs_toa_nlcd'] self.metadata = metadata[name] if name and name != "default" else metadata['oli_tirs_toa_nlcd'] name = "oli_tirs_toa_nlcd" if name is None or name == "default" else name product = name.split('_')[-1] cmap = CMAPS[product] classes = list(cmap.keys()) self.ordinal_map = np.zeros(max(cmap.keys()) + 1, dtype=np.int64) for v, k in enumerate(classes): self.ordinal_map[k] = v 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=(self.NUM_CHANNELS, self.HEIGHT, self.WIDTH), dtype="float32"), "label": datasets.Array2D(shape=(self.HEIGHT, self.WIDTH), dtype="int32"), "optical_channel_wv": datasets.Sequence(datasets.Value("float32")), "spatial_resolution": datasets.Value("int32"), }), ) def _split_generators(self, dl_manager): if isinstance(self.DATA_URL, list): downloaded_files = dl_manager.download(self.DATA_URL) combined_file = os.path.join(dl_manager.download_config.cache_dir, "combined.tar.gz") with open(combined_file, 'wb') as outfile: for part_file in downloaded_files: with open(part_file, 'rb') as infile: shutil.copyfileobj(infile, outfile) data_dir = dl_manager.extract(combined_file) os.remove(combined_file) 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 = self.metadata["channel_wv"] spatial_resolution = self.spatial_resolution data_dir = os.path.join(data_dir, "SSL4EOLBenchmark") if self.config.name in ["etm_oli_toa_nlcd", "etm_oli_toa_cdl"]: product = self.config.name.split('_')[-1] # nlcd / cdl metadata_oli = pd.read_csv(os.path.join(data_dir, f"metadata_oli_tirs_toa_{product}.csv")) metadata_etm = pd.read_csv(os.path.join(data_dir, f"metadata_etm_toa_{product}.csv")) metadata = self.sort_and_create_new_csv(metadata_oli, metadata_etm) else: metadata = pd.read_csv(os.path.join(data_dir, f"metadata_{self.config.name}.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 try: optical_path_etm = os.path.join(data_dir, row.optical_path_etm) optical_etm = self._read_image(optical_path_etm).astype(np.float32) optical = np.concatenate((optical_etm, optical), axis=0) except: pass label_path = os.path.join(data_dir, row.label_path) label = self._read_image(label_path).astype(np.int32) label = self.ordinal_map[label] sample = { "optical": optical, "label": label, "optical_channel_wv": optical_channel_wv, "spatial_resolution": spatial_resolution, } yield f"{index}", sample 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) if len(image.shape) == 3: image = np.transpose(image, (2, 0, 1)) return image def sort_and_create_new_csv(self, metadata_oli, metadata_etm): def extract_number(optical_path): return optical_path.split('/')[1] metadata_oli['number'] = metadata_oli['optical_path'].apply(extract_number) # a number metadata_etm['number'] = metadata_etm['optical_path'].apply(extract_number) # a number metadata_oli = metadata_oli.sort_values('number').reset_index(drop=True) metadata_etm = metadata_etm.sort_values('number').reset_index(drop=True) new_rows = [] i, j = 0, 0 while i < len(metadata_oli) and j < len(metadata_etm): if metadata_oli.loc[i, 'number'] == metadata_etm.loc[j, 'number']: new_rows.append({ 'split': metadata_oli.loc[i, 'split'], 'optical_path_etm': metadata_etm.loc[j, 'optical_path'], 'optical_path': metadata_oli.loc[i, 'optical_path'], 'label_path': metadata_oli.loc[i, 'label_path'] }) i += 1 j += 1 elif metadata_oli.loc[i, 'number'] < metadata_etm.loc[j, 'number']: i += 1 else: j += 1 new_df = pd.DataFrame(new_rows, columns=['split', 'optical_path_etm', 'optical_path', 'label_path']) return new_df