Paper Info
Title
Building Damage Detection Using U-Net With Attention Mechanism From Pre- And Post-Disaster Remote Sensing Datasets
Abstract
The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance among single models, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.
Year
DOI
Venue
2021
10.3390/rs13050905
REMOTE SENSING
Keywords
DocType
Volume
building damage, disaster, remote sensing image, Siamese neural network, U-Net, attention mechanism, change detection
Journal
13
Issue
Citations 
PageRank 
5
2
0.37
References 
Authors
0
8
Name
Order
Citations
PageRank
Chuyi Wu130.78
Feng Zhang2127.66
Junshi Xia320.71
Yichen Xu420.37
Guoqing Li522.40
Jibo Xie621.38
Zhenhong Du73116.98
Liu Renyi81513.13