CaBuAr P2

# California Burned Areas Dataset

This is the second 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.