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Segment Any Building

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14495))

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Abstract

The identification and segmentation of buildings in remote sensing imagery has consistently been a important point of academic research. This work highlights the effectiveness of using diverse datasets and advanced representation learning models for the purpose of building segmentation in remote sensing images. By fusing various datasets, we have broadened the scope of our learning resources and achieved exemplary performance across several datasets. Our innovative joint training process demonstrates the value of our methodology in various critical areas such as urban planning, disaster management, and environmental monitoring. Our approach, which involves combining dataset fusion techniques and prompts from pre-trained models, sets a new precedent for building segmentation tasks. The results of this study provide a foundation for future exploration and indicate promising potential for novel applications in building segmentation field.

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Acknowledgments

This work was supported by the DeepCrop project and PerformLCA project (UCPH Strategic plan 2023 Data+ Pool).

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Authors and Affiliations

  1. Department of Computer Science, University of Copenhagen, 2100, Copenhagen, Denmark

    Lei Li

Authors
  1. Lei Li
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Correspondence to Lei Li .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Li, L. (2024). Segment Any Building. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_14

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  • DOI: https://doi.org/10.1007/978-3-031-50069-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50068-8

  • Online ISBN: 978-3-031-50069-5

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