BF-SAM: enhancing SAM through multi-modal fusion for fine-grained building function identification-Reference-Cited by-同舟云学术

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BF-SAM: enhancing SAM through multi-modal fusion for fine-grained building function identification

Published:2024-09-05 Issue: Volume: Page:1-27
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Gong Zhaoya¹²^ORCID,Li Binbo¹²^ORCID,Wang Chenglong¹²^ORCID,Chen Jun³,Zhao Pengjun¹²

Affiliation:

1. School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, China

2. Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Peking University Shenzhen Graduate School, Shenzhen, China

3. National Geomatics Center of China, Beijing, China

Funder

Shenzhen Science and Technology Program

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2024.2399142

Reference92 articles.

1. Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs

2. Classification of Building Types in Germany: A Data-Driven Modeling Approach

3. Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data

4. Bodur R. Bhattarai B. and Kim T.-K. 2021. A unified architecture of semantic segmentation and hierarchical generative adversarial networks for expression manipulation. arXiv Preprint arXiv:2112.04603.

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