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FireRisk (FireRisk: A Remote Sensing Dataset for Fire Risk Assessment)

Introduced by Shuchang Shen et al. in FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning13 Mar 2023 archive 2025-07-28

In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91 872 labelled images for fire risk assessment. This remote sensing dataset is labelled with the fire risk classes supplied by the Wildfire Hazard Potential (WHP) raster dataset, and remote sensing images are collected using the National Agriculture Imagery Program (NAIP), a high-resolution remote sensing imagery program. On FireRisk, we present benchmark performance for supervised and self-supervised representations, with Masked Autoencoders (MAE) pre-trained on ImageNet1k achieving the highest classification accuracy, 65.29%.

Benchmarks archive 2025-07-28

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First row (archive order)PaperCode
Remote Sensing Image Classification FireRisk ResNet-50 Accuracy (%) 63.20 FireRisk: A Remote Sensing Dataset for Fire Risk... charmonyshen/firerisk 4 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 3. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

CC BY-NC

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • FireRisk

1 variant name, as the archive lists them.

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