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--- |
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license: apache-2.0 |
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task_categories: |
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- image-segmentation |
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tags: |
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- wildfire |
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- burnscars |
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- segmentation |
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pretty_name: CEMS Burn Scars |
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size_categories: |
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- n<1K |
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--- |
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# Dataset Card for CEMS Burn Scar |
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### Dataset Summary |
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This dataset provides Harmonized Landsat and Sentinel-2 (HLS) imagery of burn scars and associated masks for the years 2020-2024, primarily over Europe, with some non-European cases (e.g., Mexico, Guatemala). It includes 439 high-quality 512x512 scenes, designed for training geospatial machine learning models for burn scar segmentation. |
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The dataset follows the same principles and structure as the [HLS Burn Scars dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars), ensuring compatibility and consistency. |
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## Dataset Structure |
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### TIFF Metadata |
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Each TIFF file is a 512x512 pixel scene. |
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Scenes contain eight bands, and masks have one band. |
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Satellite scenes have bands in Digital Numbers (DN), as per Sentinel-Hub documentation (https://docs.sentinel-hub.com/api/latest/data/hls/#units). |
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$$ DN = 10000 \times REFLECTANCE $$ |
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All images contain **metadata** about the event, including the title corresponding to the activation in CEMS, the date range and the area. |
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## Band Order |
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For scenes: |
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| Channel | Name | HLS S30 Band number | |
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|---------|-----|---------------------| |
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|1| Blue| B02| |
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|2| Green| B03| |
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|3| Red| B04| |
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|4| NIR| B8A| |
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|5| SWIR 1| B11| |
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|6| SWIR 2| B12| |
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|7| dataMask| -| |
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|8| QA| -| |
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## Class Distribution |
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Masks are a single band with values: |
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- 1: Burn scar |
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- 0: Not burned |
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|Label | Representation| |
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|------|------| |
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|Burn Scar | 10 %| |
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|Not burned | 90 %| |
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## Data Splits |
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The 439 files are randomly split into training (70%), validation (12%), and test (18%) directories, each containing masks, scenes, and index files. |
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## Dataset Creation |
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Following the principles of the HLS Burn Scars dataset, we developed a sophisticated pipeline to transform Copernicus EMS (CEMS) burn scar geometries into high-quality image-label pairs. Key steps include: |
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- Extracting geometries from CEMS Rapid Mapping data and tiling into a UTM grid (512x512 at 30m resolution). |
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- Applying spatio-temporal clustering to group scars within a 30-day window, resolving overlaps and minimizing false negatives. |
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- Downloading HLS imagery via Sentinel-Hub, filtering out tiles with excessive cloud cover or invalid pixels. This process ensures robust data for training geospatial models. |
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## Source Data |
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Imagery is sourced from HLS V2 via Sentinel-Hub, accessible at https://www.sentinel-hub.com/. A full description of HLS is available at https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf. |
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Burn scar geometries are from CEMS Rapid Mapping (2020-2024), covering ~200 fire events, available at https://mapping.emergency.copernicus.eu/. |
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## Citation |
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If this dataset supports your research, please cite CEMS Burn Scars in your publications. Example BibTeX entry: |
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``` |
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@dataset{cems_burnscars, |
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author = {Jose Moreno Ortega}, |
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title = {CEMS BurnScars}, |
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year = {2025}, |
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month = {may}, |
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day = {4}, |
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publisher = {Zenodo}, |
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doi = {10.5281/zenodo.15335977}, |
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url = {https://doi.org/10.5281/zenodo.15335977} |
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} |
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``` |
