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SFobNet: An Improved Swin Transformer Integrating Urban Functional Zones for Object-Level Building Height Estimation From Sentinel-2 Images

文献类型：期刊论文

作者Bao, Wenxuan2,2,3; Dou, Yinyin3; Kuang, Wenhui3; Guo, Changqing2,2,3; Wei, Zhishou2,2,3; Hou, Yali2,2,3; Yin, Zherui1; Hongger, Zherui1
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2025
卷号18页码:26961-26982
关键词Estimation Buildings Feature extraction Transformers Remote sensing Urban areas Data mining Convolutional neural networks Accuracy Spatial resolution Building height estimation improved Swin Transformer object-level Sentinel-2 images urban functional zones
ISSN号1939-1404
DOI10.1109/JSTARS.2025.3619085
产权排序1
文献子类Article
英文摘要Building height is a critical parameter in modeling urban spatial structures, serving as an essential basis for interpreting urban form and evaluating spatial efficiency. Despite the remarkable progress in building height estimation, most existing methods still rely on convolutional neural network (CNN) and perform pixel-level estimation using physical features extracted from remote sensing imagery. However, these approaches often struggle to capture global structural patterns, fail to represent height heterogeneity at the individual building level, and overlook the intrinsic relationship between building functional types and height. To address these issues, this study proposes a novel deep learning method called the improved Swin Transformer integrated with urban Functional zones for Object-level Building height estimation Network (SFobNet). The proposed method utilizes an improved Swin Transformer to accurately extract the local and global features of buildings, effectively reducing systematic bias through the integration of urban functional zones, thereby achieving consistent representation of height information at the individual building level. The experimental results indicated that SFobNet achieved superior validation accuracy in Beijing, with R (2) = 0.7155 and RMSE = 8.0889 m, reducing error by 9.4% compared with the state-of-the-art SEASONet and showing clear advantages over other baseline models. Cross-city evaluations on Tianjin and Shijiazhuang further confirmed its generalization performance, achieving R (2) = 0.5058 and RMSE = 11.1161 m, while consistently outperforming SEASONet. Ablation experiments further verified the effectiveness of the proposed method in addressing the aforementioned challenges. In conclusion, SFobNet significantly enhances the precision and robustness of object-level building height estimation, offering a particularly promising and solid methodological foundation for future large-scale urban three-dimensional morphological reconstruction.
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WOS关键词DENSITY ; CHINA ; RECONSTRUCTION
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001606734900006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.igsnrr.ac.cn/handle/311030/217809]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Kuang, Wenhui
作者单位1.Inner Mongolia Normal Univ, Coll Geog Sci, Hohhot 010022, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101408, Peoples R China;
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Bao, Wenxuan,Dou, Yinyin,Kuang, Wenhui,et al. SFobNet: An Improved Swin Transformer Integrating Urban Functional Zones for Object-Level Building Height Estimation From Sentinel-2 Images[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:26961-26982.
APA Bao, Wenxuan.,Dou, Yinyin.,Kuang, Wenhui.,Guo, Changqing.,Wei, Zhishou.,...&Hongger, Zherui.(2025).SFobNet: An Improved Swin Transformer Integrating Urban Functional Zones for Object-Level Building Height Estimation From Sentinel-2 Images.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,26961-26982.
MLA Bao, Wenxuan,et al."SFobNet: An Improved Swin Transformer Integrating Urban Functional Zones for Object-Level Building Height Estimation From Sentinel-2 Images".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):26961-26982.

入库方式： OAI收割

来源：地理科学与资源研究所

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