Create crisislandmark.py

crisislandmark.py

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+import os
+
+import datasets
+import h5py
+import hdf5plugin
+import pandas as pd
+import pyarrow
+
+pyarrow.PyExtensionType.set_auto_load(True)
+
+_CITATION = """\
+@misc{cambrin2025texttoremotesensingimageretrievalrgbsources,
+      title={Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources}, 
+      author={Daniele Rege Cambrin and Lorenzo Vaiani and Giuseppe Gallipoli and Luca Cagliero and Paolo Garza},
+      year={2025},
+      eprint={2507.10403},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.10403}, 
+}
+"""
+
+_DESCRIPTION = """\
+CrisisLandMark is a large-scale, multimodal corpus for Text-to-Remote-Sensing-Image Retrieval (T2RSIR). 
+It contains over 647,000 Sentinel-1 (SAR) and Sentinel-2 (multispectral optical) images enriched with 
+structured textual and geospatial annotations. The dataset is designed to move beyond standard RGB imagery, 
+enabling the development of retrieval systems that can leverage the rich physical information from different 
+satellite sensors for applications in Land Use/Land Cover (LULC) mapping and crisis management.
+"""
+
+_HOMEPAGE = "https://github.com/DarthReca/closp"  # Replace with your actual project page if different
+
+_LICENSE = "Creative Commons Attribution Non Commercial 4.0"
+
+# Define the satellite data sources as in the original script
+_SATELLITE_DATASETS = {
+    "s2": ["benv2s2", "cabuar", "sen2flood"],
+    "s1": ["benv2s1", "mmflood", "sen1flood", "quakeset"],
+}
+
+_URLS = {"main": "crisislandmark.h5", "metadata": "metadata.parquet"}
+
+
+class CrisisLandMarkConfig(datasets.BuilderConfig):
+    """BuilderConfig for CrisisLandMark."""
+
+    def __init__(self, satellite_type, **kwargs):
+        """
+        Args:
+            satellite_type (str): Type of satellite data to load ('s1', 's2', or 'all').
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(CrisisLandMarkConfig, self).__init__(**kwargs)
+        self.satellite_type = satellite_type
+
+
+class CrisisLandMark(datasets.GeneratorBasedBuilder):
+    """CrisisLandMark Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # Configure for different satellite types
+    BUILDER_CONFIGS = [
+        CrisisLandMarkConfig(
+            name="s1",
+            version=VERSION,
+            description="Load only Sentinel-1 (SAR) images.",
+            satellite_type="s1",
+        ),
+        CrisisLandMarkConfig(
+            name="s2",
+            version=VERSION,
+            description="Load only Sentinel-2 (Optical) images.",
+            satellite_type="s2",
+        ),
+        CrisisLandMarkConfig(
+            name="all",
+            version=VERSION,
+            description="Load all images (Sentinel-1 and Sentinel-2).",
+            satellite_type="all",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "s2"
+
+    def _info(self):
+        # Define the features of the dataset
+        features = datasets.Features(
+            {
+                "key": datasets.Value("string"),
+                "image": datasets.Array3D(shape=(None, 120, 120), dtype="float32"),
+                "coords": datasets.Array3D(shape=(2, 120, 120), dtype="float32"),
+                "labels": datasets.Sequence(datasets.Value("string")),
+                "crs": datasets.Value("int64"),
+                "timestamp": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        files = dl_manager.download(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split": "train"} | files,
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split": "corpus"} | files,
+            ),
+        ]
+
+    def _generate_examples(self, split, metadata, main, **kwargs):
+        """Yields examples."""
+
+        # --- Load and filter metadata ---
+        metadata_df = pd.read_parquet(metadata)
+        metadata_df = metadata_df[metadata_df["split"] == split]
+
+        # Filter by satellite type based on the config
+        satellite_type = self.config.satellite_type
+        if satellite_type != "all":
+            satellite_filter = "|".join(_SATELLITE_DATASETS[satellite_type])
+            metadata_df = metadata_df[
+                metadata_df["key"].str.contains(satellite_filter, regex=True)
+            ]
+
+        sample_keys = metadata_df[["key", "labels"]].to_records(index=False)
+        # Open the HDF5 file once and yield examples
+        with h5py.File(main, "r") as f:
+            for key, labels in sample_keys:
+                sample_group = f[key]
+
+                # Read data
+                image_np = sample_group["image"][:].astype("float32")
+                coords_np = sample_group["coords"][:].astype("float32")
+
+                # Read attributes
+                crs = sample_group.attrs["crs"]
+                timestamp = sample_group.attrs["timestamp"]
+
+                sample = {
+                    "key": key,
+                    "image": image_np,
+                    "coords": coords_np,
+                    "labels": list(labels),  # Ensure it's a list
+                    "crs": crs,
+                    "timestamp": timestamp,
+                }
+
+                yield (key, sample)