Add dataset loading script.

hls_burn_scars.py

@@ -0,0 +1,92 @@
+import os
+
+import datasets
+
+
+_CITATION = """\
+@software{HLS_Foundation_2023,
+    author = {Phillips, Christopher and Roy, Sujit and Ankur, Kumar and Ramachandran, Rahul},
+    doi    = {10.57967/hf/0956},
+    month  = aug,
+    title  = {{HLS Foundation Burnscars Dataset}},
+    url    = {https://huggingface.co/ibm-nasa-geospatial/hls_burn_scars},
+    year   = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset contains Harmonized Landsat and Sentinel-2 imagery of burn scars and the associated masks for the years 2018-2021 over the contiguous United States. There are 804 512x512 scenes. Its primary purpose is for training geospatial machine learning models.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars"
+
+_LICENSE = "cc-by-4.0"
+
+_URLS = {
+    "burn_scars": {
+        "train/val": "https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars/resolve/main/hls_burn_scars.tar.gz"
+    }
+}
+
+class HLSBurnScars(datasets.GeneratorBasedBuilder):
+    """MIT Scene Parsing Benchmark dataset."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="hls_burn_scars", version=VERSION, description=_DESCRIPTION),
+    ]
+
+    def _info(self):    
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "annotation": datasets.Image(),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+
+        data_dirs = dl_manager.download_and_extract(urls)
+        train_data = os.path.join(data_dirs["training"], "hls_burn_scars")
+        val_data = os.path.join(data_dirs["validation"], "hls_burn_scars")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data": train_data,
+                    "split": "training",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data": val_data,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data": val_data,
+                    "split": "testing",
+                },
+            )
+        ]
+
+    def _generate_examples(self, data, split):
+        for idx, (path, file) in enumerate(data):
+            if path.endswith("_merged.tif"):
+                yield idx, {
+                    "image": {"path": path, "bytes": file.read()},
+                    "annotation": {"path": path.replace('_merged.tif', '.mask.tif'), "bytes": file.read()},
+                }