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README.md

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+---
+size_categories:
+- n<1K
+license: cc-by-4.0
+language:
+- en
+---
+
+# Dataset Card for HLS Burn Scar Scenes
+
+## Dataset Description
+
+- **Homepage: https://huggingface.co/datasets/nasa-impact/hls_burn_scars**
+- **Point of Contact: Dr. Christopher Phillips (cep0013@uah.edu)** 
+
+### Dataset Summary
+
+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.
+
+## Dataset Structure
+
+
+## TIFF Metadata
+Each tiff file contains a 512x512 pixel tiff file. Scenes contain six bands, and masks have one band. For satellite scenes, each band has already been converted to reflectance.
+
+## Band Order
+For scenes:
+Channel, Name, HLS S30 Band number  
+1, Blue,  B02  
+2, Green, B03  
+3, Red,   B04  
+4, NIR,   B8A  
+5, SW 1,  B11  
+6, SW 2,  B12  
+
+Masks are a single band with values:  
+1 = Burn scar  
+0 = Not burned  
+-1 = Missing data  
+
+## Class Distribution
+Burn Scar - 11%  
+Not burned - 88%  
+No Data - 1%  
+
+## Data Splits
+The 804 files have been randomly split into training (2/3) and validation (1/3) directories, each containing the masks, scenes, and index files.
+
+## Dataset Creation
+
+After co-locating the shapefile and HLS scene, the 512x512 chip was formed by taking a window with the burn scar in the center. Burn scars near the edges of HLS tiles are offset from the center.  
+Images were manually filtered for cloud cover and missing data to provide as clean a scene as possible, and burn scar presence was also manually verified.
+
+## Source Data
+Imagery are from V1.4 of HLS. A full description and access to HLS may be found at https://hls.gsfc.nasa.gov/  
+
+The data were from shapefiles maintained by the Monitoring Trends in Burn Severity (MTBS) group. The original data may be found at:  
+https://mtbs.gov/
+
+## Citation
+
+If this dataset helped your research, please cite `HLS Burn Scars` in your publications. Here is an example BibTeX entry:
+
+```
+@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}
+}
+```

fire_scars_train_val.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d84e413358c69724fb793da620c3158c0683dc895787ccc2e3fd2cd5a1581fa5
+size 1779237070

hls_burn_scars.py

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+import os
+from glob import glob
+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 = {
+    "hls_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['train/val'], "training")
+        val_data = os.path.join(data_dirs['train/val'], "validation")
+
+        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.TEST,
+                gen_kwargs={
+                    "data": val_data,
+                    "split": "testing",
+                },
+            )
+        ]
+
+    def _generate_examples(self, data, split):
+        files = glob(f"{data}/*_merged.tif")
+        for idx, filename in enumerate(files):
+            if filename.endswith("_merged.tif"):
+                annotation_filename = filename.replace('_merged.tif', '.mask.tif')
+                yield idx, {
+                    "image": {"path": filename}, 
+                    "annotation": {"path": annotation_filename}
+                }