Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System

计算机科学 稳健性(进化) 航空影像 深度学习 人工智能 分割 遥感 职位(财务) 计算机视觉 图像(数学) 地理 生物化学 化学 财务 经济 基因
作者
Dai Quoc Tran,Minsoo Park,Daekyo Jung,Seunghee Park
出处
期刊:Remote Sensing [Multidisciplinary Digital Publishing Institute]
卷期号:12 (24): 4169-4169 被引量:50
标识
DOI:10.3390/rs12244169
摘要

Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, the Republic of Korea, in April 2020. Then, the proposed two-patch-level deep-learning models were implemented. A patch-level 1 network was trained using the UNet++ architecture. The output prediction of this network was used as a position input for the second network, which used UNet. It took the reference position from the first network as its input and refined the results. Finally, the final performance of our proposed method was compared with a state-of-the-art image-segmentation algorithm to prove its robustness. Comparative research on the loss functions was also performed. Our proposed approach demonstrated its effectiveness in extracting burnt areas from UAV images and can contribute to estimating maps showing the areas damaged by forest fires.
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