BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode
This work addresses the need for detailed building data to inform energy efficiency and urban design, but it is incremental as it applies existing multimodal LLM tools to a new domain-specific dataset.
The researchers tackled the challenge of creating accurate urban building exterior databases by integrating Google Street View imagery and spatial data, resulting in a validated database for New York City, Amsterdam, and Singapore that supports urban planning and sustainability analysis.
Urban Building Exteriors are increasingly important in urban analytics, driven by advancements in Street View Imagery and its integration with urban research. Multimodal Large Language Models (LLMs) offer powerful tools for urban annotation, enabling deeper insights into urban environments. However, challenges remain in creating accurate and detailed urban building exterior databases, identifying critical indicators for energy efficiency, environmental sustainability, and human-centric design, and systematically organizing these indicators. To address these challenges, we propose BuildingView, a novel approach that integrates high-resolution visual data from Google Street View with spatial information from OpenStreetMap via the Overpass API. This research improves the accuracy of urban building exterior data, identifies key sustainability and design indicators, and develops a framework for their extraction and categorization. Our methodology includes a systematic literature review, building and Street View sampling, and annotation using the ChatGPT-4O API. The resulting database, validated with data from New York City, Amsterdam, and Singapore, provides a comprehensive tool for urban studies, supporting informed decision-making in urban planning, architectural design, and environmental policy. The code for BuildingView is available at https://github.com/Jasper0122/BuildingView.