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Open AccessArticle
Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery
by
Yingjie Ji
Yingjie Ji
Weiguo Wu
Weiguo Wu
Dr. Weiguo Wu is currently a Professor at the School of Electronic Information and Engineering at He [...]
Dr. Weiguo Wu is currently a Professor at the School of Electronic Information and Engineering at Xi’an Jiaotong University, China. He has been working at this university since 1986. He received a PhD at the Xi'an Jiaotong University in 2006. He is currently a Senior Member of the Chinese Computer Society, a mMember of the Microcomputers (Embedded Systems) and High-Performance Computing Committees of the Chinese Computer Society, a Member of the Expert Committee of the China Storage Industry Technology Innovation Strategy Alliance, a Director of the Shaanxi Computer Society, and has served as the Deputy Director of the New Computer Research Institute of Xi'an Jiaotong University and the Deputy Director of the Shaanxi High-Performance Computing Committee. His research interests include high-performance computer architecture, storage systems, cloud computing, and embedded systems.
,
Guangtong Wan
Guangtong Wan
Yindi Zhao
Yindi Zhao
Weilin Wang
Weilin Wang
Hui Yin
Hui Yin
Zhuang Tian
Zhuang Tian
Song Liu
Song Liu
Prof. Dr. Song Liu has been an Associate Professor at the School of Computer Science and Technology [...]
Prof. Dr. Song Liu has been an Associate Professor at the School of Computer Science and Technology of Xi'an Jiaotong University, China, since 2023. He previously worked as an Assistant Professor beginning in 2019 at this university. He received a PhD majoring in Computer Science and Technology at the Xi'an Jiaotong University in 2018. He has led and participated in many scientific research projects, such as research on machine learning operator optimization technology based on domestic chip cluster architecture, a sub-project of the National Key R&D Program; the sub-topic of the National Defense Basic Research Nuclear Science Challenge “Pattern driven Commercial CPU and GPU Numerical Kernel Floating Point Optimization Methods”; the Hangzhou Hikvision Digital Technology Co., Ltd. Horizontal Project “Deep Development of Ceph Storage Technology”, and so on. His research interests include parallel program performance optimization, high-performance computing, AI enabling and optimization technology, edge computing, etc.
1
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
2
North China Institute of Computing Technology, Beijing 100083, China
3
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
4
College of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(14), 2661; https://doi.org/10.3390/rs16142661 (registering DOI)
Submission received: 13 June 2024
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Revised: 16 July 2024
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Accepted: 16 July 2024
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Published: 20 July 2024
Abstract
With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was proposed combining LiDAR data and VHR remote sensing images in this study. Three key steps are included in this method: approximate footprint detection using LiDAR data, automatic prompt generation for the SAM, and residential building footprint extraction. By applying this method, residential building footprints were extracted in Pukou District, Nanjing, Jiangsu Province. Based on this, a comprehensive assessment model was constructed to systematically evaluate the spatial layout of urban complexes using six dimensions of assessment indicators. The results showed the following: (1) The proposed method was used to effectively extract residential building footprints. (2) The residential complexes in the study area were classified into four levels. The numbers of complexes classified as Excellent, Good, Average, and Poor were 10, 29, 16, and 1, respectively. Residential complexes of different levels exhibited varying spatial layouts and building distributions. The results provide a visual representation of the spatial distribution of residential complexes that belong to different levels within the study area, aiding in urban planning.
Share and Cite
MDPI and ACS Style
Ji, Y.; Wu, W.; Wan, G.; Zhao, Y.; Wang, W.; Yin, H.; Tian, Z.; Liu, S.
Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery. Remote Sens. 2024, 16, 2661.
https://doi.org/10.3390/rs16142661
AMA Style
Ji Y, Wu W, Wan G, Zhao Y, Wang W, Yin H, Tian Z, Liu S.
Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery. Remote Sensing. 2024; 16(14):2661.
https://doi.org/10.3390/rs16142661
Chicago/Turabian Style
Ji, Yingjie, Weiguo Wu, Guangtong Wan, Yindi Zhao, Weilin Wang, Hui Yin, Zhuang Tian, and Song Liu.
2024. "Segment Anything Model-Based Building Footprint Extraction for Residential Complex Spatial Assessment Using LiDAR Data and Very High-Resolution Imagery" Remote Sensing 16, no. 14: 2661.
https://doi.org/10.3390/rs16142661
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