Abstract
The cause of forest fire is complex, which depends on meteorological, surface soil and human integrated monitoring network and multi-source data like remote sensing. This paper provides 4 kinds of monitoring method based on satellite, IoT, UAV aviation cruise, etc. Carrying out all-round fire point detection, combined with administrative division data, forest-grassland resource data and meteorological observation data, the fire monitoring and customized analysis are carried out through computer automation, computer vision technology and deep learning algorithms, the fire thematic functions are generated. It also supports the release and display of fire information in various forms such as SMS, email, Web terminal and mobile terminal, so as to grasp the occurrence of fire points in the area at the first time, and realize the 24-h uninterrupted forest fire monitoring and early warning. Multi-channel radiation fusion, yolov4 algorithm is used for training, combined with the IoT data, the accuracy of early warning and pre-assessment improved, provides scientific decision-making basis for forest fire prevention and rescue work.
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Fan, X., Pang, X., Feng, H. (2023). Design of Multi-data Sources Based Forest Fire Monitoring and Early Warning System. In: Tekinerdogan, B., Wang, Y., Zhang, LJ. (eds) Internet of Things – ICIOT 2022. ICIOT 2022. Lecture Notes in Computer Science, vol 13735. Springer, Cham. https://doi.org/10.1007/978-3-031-23582-5_5
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DOI: https://doi.org/10.1007/978-3-031-23582-5_5
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