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科学有效地监测森林火灾，快速准确掌握火灾发生地点，进而合理避免或降低森林资源损失，对保护生态建设成果、维护生态安全具有重要意义。针对当前国内外森林火灾监测预警技术的原理与应用场景，系统阐述了开发采集森林火灾多源数据技术，构建基于多模态融合数据的火灾监测和预警深度学习模型，在单模态、双模态或多模态输入数据情况下对森林火灾进行实时精准监测与预警，可显著降低森林火灾监测和预警的误报率和漏报率。最后预测了森林火灾监测预警技术的发展趋势。
Abstract：Scientifically and effectively monitoring forest fires, quickly and accurately understanding the location of fires, and then reasonably avoiding or reducing the loss of forest resources are of great significance for protecting the achievements of ecological construction and maintaining ecological security. Based on the principles and application scenarios of current forest fire monitoring and early warning technologies at home and abroad, in this paper, the development of multi-source data collection technology for forest fires was systematically expounded, a deep learning model for fire monitoring and early warning based on multi-modal fusion data was constructed, and real-time and accurate monitoring and early warning of forest fires under single-modal, dual-modal, or multi-modal input data conditions was achieved. This could significantly reduce the false alarm rate and missed alarm rate of forest fire monitoring and early warning. Finally, an outlook and prediction on the future development trends of forest fire monitoring and early warning technology was provided in this paper.
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基本信息:
中图分类号:S762.2
引用信息:
[1]罗碧珍,吴冠霖.森林火灾监测预警智能化技术研究进展[J].森林防火,2025,43(06):15-18.
基金信息:
广东司法警官职业学院院级课题（2023ZD02）; 广东省普通高校特色创新类项目（2023WTSCX222、2024WTSCX289）