A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification
Quick Answer
The GWFP dataset is a large-scale, open-source collection of wildfire images and videos aimed at enhancing early detection and monitoring.
Quick Take
The GWFP dataset is a large-scale, open-source collection of wildfire images and videos aimed at enhancing early detection and monitoring. It includes diverse scenes and benchmarks various architectures, demonstrating strong cross-dataset generalization for real-world applications. The dataset will be publicly released upon acceptance.
Key Points
- GWFP includes diverse wildfire scenes, including flames, smoke, and challenging negative samples.
- Multiple convolutional and transformer architectures were benchmarked for dataset robustness.
- HTE-ResNet was explored for lightweight frequency-spatial feature interaction.
- Experimental results show strong generalization across datasets for wildfire monitoring.
- The dataset and source code will be publicly available upon acceptance.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 10174v1 Announce Type: new Abstract: Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we introduce GWFP (Global Wildfire Prevention Dataset), a large-scale, open-source dataset of wildfire images and videos designed to support early fire and smoke detection research.
GWFP contains geographically diverse wildfire scenes, including flames, smoke, Waterdog/Fog environmental conditions, Near Infrared (NIR) imagery, Ember, and challenging negative samples collected from real-world scenarios worldwide. To evaluate dataset robustness and cross-domain generalization, we benchmark multiple convolutional and transformer-based architectures across both in-domain and cross-dataset settings.
Additionally, we explore lightweight frequency--spatial feature interaction using Hadamard-enhanced residual connections (HTE-ResNet) to analyze representation robustness under domain-shift conditions. Experimental results demonstrate strong cross-dataset generalization and practical utility for real-world wildfire monitoring applications. The dataset and source code will be publicly released upon acceptance.
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