CaBuAr P1

<pre># California Burned Areas Dataset This is the first part of the dataset. ### Dataset Summary This dataset contains images from Sentinel-2 satellites taken before and after a wildfire. The ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images. ### Supported Tasks The dataset is designed to do binary semantic segmentation of burned vs unburned areas. ## Dataset Structure ### Dataset opening Dataset was compressed using `h5py` and BZip2 from `hdf5plugin`. **WARNING: `hdf5plugin` is necessary to extract data** ### Data Instances 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. ### Data Fields In each HDF5 file, you can find post-fire, pre-fire images and binary masks. The file is structured in this way: ```bash ├── foldn │ ├── uid0 │ │ ├── pre_fire │ │ ├── post_fire │ │ ├── mask │ ├── uid1 │ ├── post_fire │ ├── mask │ ├── foldm ├── uid2 │ ├── post_fire │ ├── mask ├── uid3 ├── pre_fire ├── post_fire ├── mask ... ``` where `foldn` and `foldm` are fold names and `uidn` is a unique identifier for the wilfire. ### Data Splits There are 5 random splits whose names are: 0, 1, 2, 3 and 4. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization Data are collected directly from Copernicus Open Access Hub through the API. The band files are aggregated into one single matrix. </pre>