TBFH: A Total-Building-Focused Hybrid Dataset for Remote Sensing Image Building Detection Article Swipe
Lin Yi
,
Feng Wang
,
Guangyao Zhou
,
Niangang Jiao
,
Minglin He
,
Jingxing Zhu
,
Hongjian You
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17132316
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17132316
Building extraction plays a crucial role in a variety of applications, including urban planning, high-precision 3D reconstruction, and environmental monitoring. In particular, the accurate detection of tall buildings is essential for reliable modeling and analysis. However, most existing building-detection methods are primarily trained on datasets dominated by low-rise structures, resulting in degraded performance when applied to complex urban scenes with high-rise buildings and severe occlusions. To address this limitation, we propose TBFH (Total-Building-Focused Hybrid), a novel dataset specifically designed for building detection in remote sensing imagery. TBFH comprises a diverse collection of tall buildings across various urban environments and is integrated with the publicly available WHU Building dataset to enable joint training. This hybrid strategy aims to enhance model robustness and generalization across varying urban morphologies. We also propose the KTC metric to quantitatively evaluate the structural integrity and shape fidelity of building segmentation results. We evaluated the effectiveness of TBFH on multiple state-of-the-art models, including UNet, UNetFormer, ABCNet, BANet, FCN, DeepLabV3, MANet, SegFormer, and DynamicVis. Our comparative experiments conducted on the Tall Building dataset, the WHU dataset, and TBFH demonstrated that models trained with TBFH significantly outperformed those trained on individual datasets, showing notable improvements in IoU, F1, and KTC scores as well as in the accuracy of building shape delineation. These findings underscore the critical importance of incorporating tall building-focused data to improve both detection accuracy and generalization performance.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17132316
- https://www.mdpi.com/2072-4292/17/13/2316/pdf?version=1751773945
- OA Status
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- References
- 32
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4412067597Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17132316Digital Object Identifier
- Title
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TBFH: A Total-Building-Focused Hybrid Dataset for Remote Sensing Image Building DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-06Full publication date if available
- Authors
-
Lin Yi, Feng Wang, Guangyao Zhou, Niangang Jiao, Minglin He, Jingxing Zhu, Hongjian YouList of authors in order
- Landing page
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https://doi.org/10.3390/rs17132316Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/17/13/2316/pdf?version=1751773945Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/17/13/2316/pdf?version=1751773945Direct OA link when available
- Concepts
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Remote sensing, Computer science, Environmental science, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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