A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments
文献类型:期刊论文
作者 | He, Jiaxin5; Cheng, Yong4; Wang, Wei5; Ren, Zhoupeng3; Zhang, Ce2; Zhang, Wenjie1 |
刊名 | REMOTE SENSING
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出版日期 | 2024-03-01 |
卷号 | 16期号:5页码:740 |
关键词 | remote sensing images lightweight context information adaptive recovery building extraction |
DOI | 10.3390/rs16050740 |
产权排序 | 3 |
文献子类 | Article |
英文摘要 | High-spatial-resolution urban buildings play a crucial role in urban planning, emergency response, and disaster management. However, challenges such as missing building contours due to occlusion problems (occlusion between buildings of different heights and buildings obscured by trees), uneven contour extraction due to mixing of building edges with other feature elements (roads, vehicles, and trees), and slow training speed in high-resolution image data hinder efficient and accurate building extraction. To address these issues, we propose a semantic segmentation model composed of a lightweight backbone, coordinate attention module, and pooling fusion module, which achieves lightweight building extraction and adaptive recovery of spatial contours. Comparative experiments were conducted on datasets featuring typical urban building instances in China and the Mapchallenge dataset, comparing our method with several classical and mainstream semantic segmentation algorithms. The results demonstrate the effectiveness of our approach, achieving excellent mean intersection over union (mIoU) and frames per second (FPS) scores on both datasets (China dataset: 85.11% and 110.67 FPS; Mapchallenge dataset: 90.27% and 117.68 FPS). Quantitative evaluations indicate that our model not only significantly improves computational speed but also ensures high accuracy in the extraction of urban buildings from high-resolution imagery. Specifically, on a typical urban building dataset from China, our model shows an accuracy improvement of 0.64% and a speed increase of 70.03 FPS compared to the baseline model. On the Mapchallenge dataset, our model achieves an accuracy improvement of 0.54% and a speed increase of 42.39 FPS compared to the baseline model. Our research indicates that lightweight networks show significant potential in urban building extraction tasks. In the future, the segmentation accuracy and prediction speed can be further balanced on the basis of adjusting the deep learning model or introducing remote sensing indices, which can be applied to research scenarios such as greenfield extraction or multi-class target extraction. |
WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:001227130300001 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/205214] ![]() |
专题 | 资源与环境信息系统国家重点实验室_外文论文 |
通讯作者 | Ren, Zhoupeng |
作者单位 | 1.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China 2.Univ Bristol, Sch Geog Sci, Bristol BS8 1SS, England 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Nanjing Univ Informat Sci & Technol, Sch Software, Nanjing 210044, Peoples R China 5.Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Peoples R China |
推荐引用方式 GB/T 7714 | He, Jiaxin,Cheng, Yong,Wang, Wei,et al. A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments[J]. REMOTE SENSING,2024,16(5):740. |
APA | He, Jiaxin,Cheng, Yong,Wang, Wei,Ren, Zhoupeng,Zhang, Ce,&Zhang, Wenjie.(2024).A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments.REMOTE SENSING,16(5),740. |
MLA | He, Jiaxin,et al."A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments".REMOTE SENSING 16.5(2024):740. |
入库方式: OAI收割
来源:地理科学与资源研究所
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