TY - GEN

T1 - Segment Any Building

AU - Li, Lei

N1 - Publisher Copyright: © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Image Segmentation

KW - Remote Sensing

U2 - 10.1007/978-3-031-50069-5_14

DO - 10.1007/978-3-031-50069-5_14

M3 - Article in proceedings

AN - SCOPUS:85184280556

SN - 9783031500688

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 155

EP - 166

BT - Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings

A2 - Sheng, Bin

A2 - Bi, Lei

A2 - Kim, Jinman

A2 - Magnenat-Thalmann, Nadia

A2 - Thalmann, Daniel

PB - Springer

T2 - 40th Computer Graphics International Conference, CGI 2023

Y2 - 28 August 2023 through 1 September 2023

ER -