Unrestricted region and scale: Deep self-supervised building mapping framework across different cities from five continents
文献类型：期刊论文
| 作者 | Zhu, Qiqi1,2; Li, Zhen1; Song, Tianjian1; Yao, Ling3; Guan, Qingfeng1,2; Zhang, Liangpei4 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 209页码:344-367 |
| 关键词 | Deep learning Label -free Building mapping and update Architectural landscape pattern changes Knowledge transfer |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2024.01.021 |
| 通讯作者 | Guan, Qingfeng(guanqf@cug.edu.cn) |
| 英文摘要 | Building footprint information is crucial for comprehending global urban development processes. Deep learning algorithms have shown significant potential in building extraction from high spatial resolution imagery. However, the requirement for large-scale annotated data has been a limitation for applying deep learning methods to city-level or national-level building mapping. The dynamic change and distinct landscape variation of cities in different geographic locations further emphasizes the need for automatic building footprint extraction. In this paper, we propose a Self-supervised Knowledge Transfer (SKTrans) framework for extracting building footprints from unlabeled remote sensing images over large areas. To address building tone differences cross regions and time periods, a tone shift mechanism is introduced to reduce the contrast, brightness, and saturation differences between buildings. Furthermore, considering building style diversity across different cities, a knowledge integration module is proposed to develop a comprehensive depiction including tonal differences, multi-scale variance, structural attributes and semantic confusion between roads and buildings. The effectiveness of SKTrans is demonstrated on three public datasets of WHU, Massachusetts, and Inria. To further evaluate the feasibility of large-scale mapping for automatic building footprint, global generalization experiments cross regions and time periods, which is based on imagery from five countries of different continents were implemented. The comprehensive results demonstrate that SKTrans surpasses existing state-of-the-art methods, supporting the time-series city-level building mapping without labeled dataset. Additionally, the architectural landscape pattern changes can be quantitatively analyzed based on the generalization results to facilitate sustainable urban development. Overall, SKTrans provides a new insight to meet the needs of high-precision label-free building mapping and update from large-scale multi-style remote sensing images. |
| WOS关键词 | GENERATIVE ADVERSARIAL NETWORKS ; CONVOLUTIONAL NEURAL-NETWORK ; SEMANTIC SEGMENTATION ; EXTRACTION ; LIDAR ; NET |
| 资助项目 | National Key Research and Development Program of China[2022YFB3903402] ; National Natural Science Foundation of China[42271413] |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001199101400001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/204744]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Guan, Qingfeng |
| 作者单位 | 1.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430078, Peoples R China 2.China Univ Geosci, Natl Engn Res Ctr GIS, Wuhan 430078, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Qiqi,Li, Zhen,Song, Tianjian,et al. Unrestricted region and scale: Deep self-supervised building mapping framework across different cities from five continents[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2024,209:344-367. |
| APA | Zhu, Qiqi,Li, Zhen,Song, Tianjian,Yao, Ling,Guan, Qingfeng,&Zhang, Liangpei.(2024).Unrestricted region and scale: Deep self-supervised building mapping framework across different cities from five continents.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,209,344-367. |
| MLA | Zhu, Qiqi,et al."Unrestricted region and scale: Deep self-supervised building mapping framework across different cities from five continents".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 209(2024):344-367. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。