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.

本研究解决了城市建筑外观数据库构建中的准确性和细节问题，强调了能源效率、环境可持续性和以人为本设计的关键指标。提出的BuildingView方法结合了Google街景的高分辨率视觉数据和OpenStreetMap的空间信息，显著提高了城市研究中的数据准确性和系统性。该数据库经过纽约市、阿姆斯特丹和新加坡的数据验证，为城市规划、建筑设计和环境政策提供了全面支持。

BuildingView：利用街景影像和多模态大型语言模型构建城市建筑外观数据库