[Submitted on 8 Jun 2026 (v1), last revised 1 Nov 2026 (this version, v3)]
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 is available on Kaggle: this https URL.
Submission history
From: Emadeldeen Hamdan [view email]
[v1] Mon, 8 Jun 2026 21:07:53 UTC (2,126 KB)
[v2] Wed, 16 Sep 2026 03:39:31 UTC (2,126 KB)
[v3] Sun, 1 Nov 2026 02:24:57 UTC (2,126 KB)
