BuildingMultiView:powering multi-scale building characterization with large language models and Multi-perspective imagery

Buildings play a crucial role in shaping urban environments, influencing their physical, functional, and aesthetic characteristics. However, urban analytics is frequently limited by datasets lacking essential semantic details as well as fragmentation across diverse and incompatible data sources. To address these challenges, we conducted a comprehensive meta-analysis of 6,285 publications (2019–2024). From this review, we identified 11 key visually discernible building characteristics grouped into three branches: satellite house, satellite neighborhood, and street-view. Based on this structured characteristic system, we introduce BuildingMultiView, an innovative framework leveraging fine-tuned Large Language Models (LLMs) to systematically extract semantically detailed building characteristics from integrated satellite and street-view imagery. Using structured image–prompt–label triplets, the model efficiently annotates characteristics at multiple spatial scales. These characteristics include swimming pools, roof types, building density, wall–window ratio, and property types. Together, they provide a comprehensive and multi-perspective building database. Experiments conducted across five cities in the USA with diverse architecture and urban form, San Francisco, San Diego, Salt Lake City, Austin, and New York City, demonstrate significant performance improvements, with an F1 score of 79.77% compared to the untuned base version of ChatGPT’s 45.66%. These results reveal diverse urban building patterns and correlations between architectural and environmental characteristics, showcasing the framework’s capability to analyze both macro-scale and micro-scale urban building data. By integrating multi-perspective data sources with cutting-edge LLMs, BuildingMultiView enhances building data extraction, offering a scalable tool for urban planners to address sustainability, infrastructure, and human-centered design, enabling smarter, resilient cities.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2026-01-06
DOI
https://doi.org/10.1016/j.jag.2025.105034
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
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article

BuildingMultiView:powering multi-scale building characterization with large language models and Multi-perspective imagery

Filip Biljecki, Yunlei Su, Wufan Zhao, Zongrong Li
International Journal of Applied Earth Observation and Geoinformation
Human Mobility and Location-Based Analysis
article

BuildingMultiView:powering multi-scale building characterization with large language models and Multi-perspective imagery

Filip Biljecki, Yunlei Su, Wufan Zhao, Zongrong Li
article en

Abstract

Buildings play a crucial role in shaping urban environments, influencing their physical, functional, and aesthetic characteristics. However, urban analytics is frequently limited by datasets lacking essential semantic details as well as fragmentation across diverse and incompatible data sources. To address these challenges, we conducted a comprehensive meta-analysis of 6,285 publications (2019–2024). From this review, we identified 11 key visually discernible building characteristics grouped into three branches: satellite house, satellite neighborhood, and street-view. Based on this structured characteristic system, we introduce BuildingMultiView, an innovative framework leveraging fine-tuned Large Language Models (LLMs) to systematically extract semantically detailed building characteristics from integrated satellite and street-view imagery. Using structured image–prompt–label triplets, the model efficiently annotates characteristics at multiple spatial scales. These characteristics include swimming pools, roof types, building density, wall–window ratio, and property types. Together, they provide a comprehensive and multi-perspective building database. Experiments conducted across five cities in the USA with diverse architecture and urban form, San Francisco, San Diego, Salt Lake City, Austin, and New York City, demonstrate significant performance improvements, with an F1 score of 79.77% compared to the untuned base version of ChatGPT’s 45.66%. These results reveal diverse urban building patterns and correlations between architectural and environmental characteristics, showcasing the framework’s capability to analyze both macro-scale and micro-scale urban building data. By integrating multi-perspective data sources with cutting-edge LLMs, BuildingMultiView enhances building data extraction, offering a scalable tool for urban planners to address sustainability, infrastructure, and human-centered design, enabling smarter, resilient cities.

International Journal of Applied Earth Observation and GeoinformationVol. 146
Sustainable cities and communities, Industry, innovation and infrastructure
Openalex Percentile: Top 95%
Human Mobility and Location-Based Analysis
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BuildingMultiView:powering multi-scale building characterization with large language models and Multi-perspective imagery — Filip Biljecki, Yunlei Su, et al. · International Journal of Applied Earth Observation and Geoinformation (2026) | TGRS Research Map | TGRS