| 引入空间距离信息的城郊山区道路提取与应用 |
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| 作者姓名： | 陈若男 彭玲 刘玉菲 卫志超 吕蓓茹 陈德跃 |
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| 作者单位： | 1. 中国科学院空天信息创新研究院, 北京 100094;2. 中国科学院大学, 北京 100049;3. 北京工业大学信息学部, 北京 100124 |
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| 基金项目： | 北京市科技计划课题（Z191100001419002）资助 |
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| 摘 要： | 近年来城郊山区成为城镇居民郊游佳选，而密集性的旅客出游及村民生产活动给山林带来火灾安全隐患。道路信息是森林防火应急信息化核心要素之一，但因城郊山区道路存在遮挡、阴影、路窄且多分支等问题，使得常规道路提取算法在城郊山区效果欠佳。故提出一种道路语义分割模型，以及一种将道路二类问题转化成多类问题的语义分割模型训练方法，迫使模型侧重学习空间距离信息，以生成空间连续性更优的道路结果。在本研究自主研制的城郊山区Yajishan道路数据集和公开数据集Massachusetts道路数据集上验证本文模型及训练方法的有效性。此外，验证该训练方法同样适用于U-Net、DeepLabV3等常用语义分割模型。还基于道路提取结果进行后处理，输出道路面、道路中心线矢量数据及道路宽度信息，并在北京丫髻山进行消防车通行性分析。研究成果在一定程度上缓解了商业电子地图在城郊山区少人处道路信息不足的问题，为森林防火应急救援提供信息化技术支撑。
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| 关 键 词： | 城郊山区 深度学习 道路信息提取 高分辨率遥感影像 |
| 收稿时间： | 2020-10-30 |
| 修稿时间： | 2021-01-08 |
Road information extraction and application in the suburban mountainous area based on remote sensing images |
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| Authors: | CHEN Ruonan PENG Ling LIU Yufei WEI Zhichao LYU Beiru CHEN Deyue |
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| Institution: | 1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Ministry of Informatics, Beijing University of Technology, Beijing 100124, China |
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| Abstract: | 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. |
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| Keywords: | suburban mountainous area deep learning road information extraction high-resolution remote sensing image |
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