Papers › FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using...
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning
Shuchang Shen, Sachith Seneviratne, Xinye Wanyan, Michael Kirley
In recent decades, wildfires, as widespread and extremely destructive natural disasters, have caused tremendous property losses and fatalities, as well as extensive damage to forest ecosystems. Many fire risk assessment projects have been proposed to prevent wildfires, but GIS-based methods are inherently challenging to scale to different geographic areas due to variations in data collection and local conditions. Inspired by the abundance of publicly available remote sensing projects and the burgeoning development of deep learning in computer vision, our research focuses on assessing fire risk using remote sensing imagery. In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91872 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%. This remote sensing dataset, FireRisk, provides a new direction for fire risk assessment, and we make it publicly available on https://github.com/CharmonyShen/FireRisk.
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| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Remote Sensing Image Classification | FireRisk | ResNet-50 | Accuracy (%) | 63.20 | #1 of 4 | Archive leaderboard | report |
| Remote Sensing Image Classification | FireRisk | ViT-B/16 | Accuracy (%) | 63.31 | #2 of 4 | Archive leaderboard | report |
| Remote Sensing Image Classification | FireRisk | DINO (ViT-B/16) | Accuracy (%) | 63.36 | #3 of 4 | Archive leaderboard | report |
| Remote Sensing Image Classification | FireRisk | MAE (ViT-B/16) | Accuracy (%) | 65.29 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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