In recent years, suburban mountain areas have become a good choice for urban residents to go outing. Intensive tourist outings and villagers' production activities bring fire safety hazards to mountains and forests. And road information is vital information for forest fire prevention emergency. However, due to the problems of occlusion, shadow, narrow and multiple branches in suburban mountainous roads, conventional urban road extraction algorithms have poor performance in suburban mountain areas. This paper proposes a road semantic segmentation model and a training method that transforms the binary into a multi-class classification problem, forcing the model to focus on learning spatial distance information to generate road results with better spatial continuity. Then, experiments were carried out on the Yajishan road dataset made by ourselves and the Massachusetts public road dataset respectively to verify the effectiveness of our model and training method. In addition, it is verified that the training method is also applicable to other commonly used semantic segmentation models such as U-Net and DeepLabV3. Finally, this paper also conducts post-processing research based on the above road extraction results to output road surface, road centerline vector data with road width information, and conducts fire truck traffic analysis application in Beijing Yaji Mountain. The research results have alleviated the problem of insufficient road information for commercial electronic maps in the suburban mountain areas with few people, and provided information technology support for forest fire emergency rescue.
CHEN Ruonan
,
PENG Ling
,
LIU Yufei
,
WEI Zhichao
,
LYU Beiru
,
CHEN Deyue
. Road information extraction and application in the suburban mountainous area based on remote sensing images[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(5)
: 658
-667
.
DOI: 10.7523/j.ucas.2021.0004
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