Publications

Zhang, Q; Zhu, J; Dong, YS; Zhao, EY; Song, MP; Yuan, QQ (2025). 10-minute forest early wildfire detection: Fusing multi-type and multi-source information via recursive transformer. NEUROCOMPUTING, 616, 128963.

Abstract
Forest wildfire has great impacts on both nature and human society. While disrupts the ecosystems, wildfire leads to significant economic loss and poses a threat to local communities. To detect forest wildfire, remote sensing technology has become an essential and powerful tool. Compared with polar-orbiting satellite, the new generation of geostationary satellite provides higher temporal resolution and faster response capability. In this study, we utilize the near real-time data of Himawari-8/9 satellite, to achieve 10-min forest early wildfire detection. A recursive transformer model is proposed in this work. It fuses multi-type and multisource information for Himawari-8/9 satellite. By leveraging the spectral, temporal and spatial features of fire pixels and considering land cover information, the proposed method reduces interference factors like cloud and terrain, resulting in minute-level and near real-time detection of forest wildfire. In 21 ground truth forest wildfire scenarios and MODIS-based cross-validation dataset, the proposed method achieves better results compared to the JAXA wildfire product, in terms of overall fire detection accuracy, early fire detection rate, omission rate, and real-time performance. Furthermore, the proposed framework effectively lowers the emergency response time for early forest wildfire detection, thereby reducing the loss caused by forest wildfire.

DOI:
10.1016/j.neucom.2024.128963

ISSN:
0925-2312