Segment Any Building

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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.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings
EditorsBin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann
Publication date2024
ISBN (Print)9783031500688
Publication statusPublished - 2024
Event40th Computer Graphics International Conference, CGI 2023 - Shanghai, China
Duration: 28 Aug 20231 Sep 2023


Conference40th Computer Graphics International Conference, CGI 2023
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

    Research areas

  • Image Segmentation, Remote Sensing

ID: 385798292