license: openrail
task_categories:
- image-segmentation
pretty_name: California Burned Areas
size_categories:
- n<1K
California Burned Areas Dataset
Dataset Description
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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:
├── 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
Curation Rationale
[More Information Needed]
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.
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
[More Information Needed]