---
language: en
license: unknown
task_categories:
- image-classification
paperswithcode_id: firerisk
pretty_name: FireRisk
tags:
- remote-sensing
- earth-observation
- geospatial
- aerial-imagery
- land-cover-classification
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': high
          '1': low
          '2': moderate
          '3': non-burnable
          '4': very_high
          '5': very_low
          '6': water
  splits:
  - name: train
    num_bytes: 11575141474.625
    num_examples: 70331
  download_size: 11575727336
  dataset_size: 11575141474.625
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# FireRisk

<!-- Dataset thumbnail -->
![FireRisk](./thumbnail.jpg)

<!-- Provide a quick summary of the dataset. -->
The FireRisk dataset is a dataset for remote sensing fire risk classification.
- **Paper:** https://arxiv.org/abs/2303.07035
- **Homepage:** https://github.com/CharmonyShen/FireRisk

## Description

<!-- Provide a longer summary of what this dataset is. -->


- **Total Number of Images**: 91872
- **Bands**: 3 (RGB)
- **Image Size**: 320x320
- **101,878 tree annotations**
- **Image Resolution**: 1m
- **Land Cover Classes**: 7
- **Classes**: high, low, moderate, non-burnable, very_high, very_low, water
- **Source**: NAIP Aerial


## Usage

To use this dataset, simply use `datasets.load_dataset("blanchon/FireRisk")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
FireRisk = load_dataset("blanchon/FireRisk")
```

## Citation 

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:


```bibtex
@article{shen2023firerisk,
  title   = {FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning},
  author  = {Shuchang Shen and Sachith Seneviratne and Xinye Wanyan and Michael Kirley},
  year    = {2023},
  journal = {arXiv preprint arXiv: 2303.07035}
}
```