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--- |
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license: openrail |
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task_categories: |
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- image-segmentation |
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pretty_name: California Burned Areas |
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size_categories: |
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- n<1K |
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tags: |
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- climate |
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--- |
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# California Burned Areas Dataset |
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**Working on adding more data** |
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## Dataset Description |
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- **Paper:** [Pre-Print](https://arxiv.org/abs/2401.11519) and [Version of Record](https://ieeexplore.ieee.org/document/10261881) |
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### Dataset Summary |
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This dataset contains images from Sentinel-2 satellites taken before and after a wildfire. |
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The ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images. |
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### Supported Tasks |
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The dataset is designed to do binary semantic segmentation of burned vs unburned areas. |
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## Dataset Structure |
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We opted to use HDF5 to grant better portability and lower file size than GeoTIFF. |
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### Dataset opening |
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Using the dataset library, you download only the pre-patched raw version for simplicity. |
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```python |
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from dataset import load_dataset |
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# There are two available configurations, "post-fire" and "pre-post-fire." |
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dataset = load_dataset("DarthReca/california_burned_areas", name="post-fire") |
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``` |
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The dataset was compressed using `h5py` and BZip2 from `hdf5plugin`. **WARNING: `hdf5plugin` is necessary to extract data**. |
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### Data Instances |
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Each matrix has a shape of 5490x5490xC, where C is 12 for pre-fire and post-fire images, while it is 0 for binary masks. |
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Pre-patched version is provided with matrices of size 512x512xC, too. In this case, only mask with at least one positive pixel is present. |
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You can find two versions of the dataset: _raw_ (without any transformation) and _normalized_ (with data normalized in the range 0-255). |
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Our suggestion is to use the _raw_ version to have the possibility to apply any wanted pre-processing step. |
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### Data Fields |
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In each standard HDF5 file, you can find post-fire, pre-fire images, and binary masks. The file is structured in this way: |
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```bash |
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├── foldn |
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│ ├── uid0 |
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│ │ ├── pre_fire |
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│ │ ├── post_fire |
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│ │ ├── mask |
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│ ├── uid1 |
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│ ├── post_fire |
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│ ├── mask |
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│ |
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├── foldm |
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├── uid2 |
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│ ├── post_fire |
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│ ├── mask |
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├── uid3 |
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├── pre_fire |
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├── post_fire |
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├── mask |
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... |
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``` |
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where `foldn` and `foldm` are fold names and `uidn` is a unique identifier for the wildfire. |
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For the pre-patched version, the structure is: |
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```bash |
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root |
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|-- uid0_x: {post_fire, pre_fire, mask} |
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|-- uid0_y: {post_fire, pre_fire, mask} |
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|-- uid1_x: {post_fire, mask} |
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... |
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``` |
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the fold name is stored as an attribute. |
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### Data Splits |
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There are 5 random splits whose names are: 0, 1, 2, 3, and 4. |
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### Source Data |
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Data are collected directly from Copernicus Open Access Hub through the API. The band files are aggregated into one single matrix. |
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## Additional Information |
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### Licensing Information |
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This work is under OpenRAIL license. |
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### Citation Information |
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If you plan to use this dataset in your work please give the credit to Sentinel-2 mission and the California Department of Forestry and Fire Protection and cite using this BibTex: |
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``` |
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@ARTICLE{cabuar, |
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author={Cambrin, Daniele Rege and Colomba, Luca and Garza, Paolo}, |
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journal={IEEE Geoscience and Remote Sensing Magazine}, |
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title={CaBuAr: California burned areas dataset for delineation [Software and Data Sets]}, |
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year={2023}, |
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volume={11}, |
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number={3}, |
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pages={106-113}, |
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doi={10.1109/MGRS.2023.3292467} |
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} |
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``` |
