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Global self−attention remote sensing building extraction network combined with edge enhancement
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Zhen LI1, Zhenxin ZHANG1, Tao WANG2, Xueli PENG3, Guijie YUE4, Deyu ZHANG2, Xianlin LIU2, 5, *, Jianhua LI6
Science & Technology Review | 2025, 43(13) : 69 - 77
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Science & Technology Review | 2025, 43(13): 69-77
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Global self−attention remote sensing building extraction network combined with edge enhancement
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Zhen LI1, Zhenxin ZHANG1, Tao WANG2, Xueli PENG3, Guijie YUE4, Deyu ZHANG2, Xianlin LIU2, 5, *, Jianhua LI6
Affiliations
  • 1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
  • 2. Beijing Geo−Vision Information Technology Co., Ltd., Beijing 100070, China
  • 3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 4. Beijing Polytechnic College, Beijing 100144, China
  • 5. North China University of Water Resources and Electric Power, Zhengzhou 450045, China
  • 6. Zhongguancun Smart City Co., Ltd., Beijing 100081, China
Published: 2025-07-13 doi: 10.3981/j.issn.1000-7857.2024.01.00025
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The accurate and efficient extraction of building from remote sensing images is fundamental for applications such as fine urban management, high−precision mapping, and land resource investigation. It is essential to investigate how to leverage image features for intelligent interpretation. This study introduces a global self−attention network with edge−enhancement (E−GSANet) for remote sensing building extraction. The network integrate the edge enhancement module into the encoder backbone, providing the network with a priori knowledge about boundaries, and then establish long−distance dependency relationships between features using the global self−attention feature expression module, enabling the fusion of salient features with edge−enhanced features. A stepwise up−sampling decoding module is designed to fusing the shallow features with rich spatial detail information and the deep features with high−order semantic information to obtain accurate extraction results of buildings. The comparison experiments between E−GSANet and the current mainstream methods is conducted based on two open−source remote sensing building datasets. The quantitative analysis and qualitative demonstrations prove that E−GSANet achieves optimal results across all evaluation metrics, yielding more complete building extractions, precise edges, and higher accuracy. Additionally, network structure ablation experiments and analysis demonstrate the effectiveness of each module.

remote sensing image  /  deep learning  /  edge enhancement  /  global self−attention  /  building extraction
Zhen LI, Zhenxin ZHANG, Tao WANG, Xueli PENG, Guijie YUE, Deyu ZHANG, Xianlin LIU, Jianhua LI. Global self−attention remote sensing building extraction network combined with edge enhancement[J]. Science & Technology Review, 2025 , 43 (13) : 69 -77 . DOI: 10.3981/j.issn.1000-7857.2024.01.00025
Year 2025 volume 43 Issue 13
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Article Info
doi: 10.3981/j.issn.1000-7857.2024.01.00025
  • Receive Date:2024-01-03
  • Online Date:2025-12-16
  • Published:2025-07-13
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History
  • Received:2024-01-03
  • Revised:2024-09-26
Funding
Affiliations
    1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
    2. Beijing Geo−Vision Information Technology Co., Ltd., Beijing 100070, China
    3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    4. Beijing Polytechnic College, Beijing 100144, China
    5. North China University of Water Resources and Electric Power, Zhengzhou 450045, China
    6. Zhongguancun Smart City Co., Ltd., Beijing 100081, China
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表12种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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