metadata

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

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

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").

from datasets import load_dataset
FireRisk = load_dataset("blanchon/FireRisk")

Citation

If you use the EuroSAT dataset in your research, please consider citing the following publication:

@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}
}