Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Fire

article

Rafik Ghali¹  Moulay A. Akhloufi¹ 

[1] Perception, Robotics and Intelligent Machines ,(PRIME), Department of Computer Science, Université de Moncton

关键词: fire detection;    fire mapping;    fire spread;    damage severity;    smoke;    wildfire;    satellite;    deep learning;   

DOI  :  10.3390/fire6050192

学科分类：环境科学（综合）

来源: mdpi

【 摘 要 】

Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were developed to detect and map wildland fires, estimate their severity, and predict their spread. In this paper, we provide a comprehensive review of recent deep learning techniques for detecting, mapping, and predicting wildland fires using satellite remote sensing data. We begin by introducing remote sensing satellite systems and their use in wildfire monitoring. Next, we review the deep learning methods employed for these tasks, including fire detection and mapping, severity estimation, and spread prediction. We further present the popular datasets used in these studies. Finally, we address the challenges faced by these models to accurately predict wildfire behaviors, and suggest future directions for developing reliable and robust wildland fire models.

【 授权许可】

CC BY