Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height, and structure, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Existing large-scale building datasets lack accuracy, extensibility and indicative attributes. This paper presents a geospatial artificial intelligence (GeoAI) framework for large-scale building modeling, introducing the first Multi-Attribute Building dataset (CMAB) in China at a national scale. The dataset covers 3,667 natural cities with a total rooftop area of 21.3 billion square meters with an F1-Score of 89.93% in rooftop extraction through the OCRNet. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating building features such as morphology, location, and function. Using multi-source data, including billions of high-resolution Google Earth imagery and 60 million street view images (SVI), we generated rooftop, height, function, age, and quality attributes for each building. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation. The results support urban planning and sustainable development.

本研究解决了现有建筑数据集在准确性和可拓展性方面的不足，提出了全国范围内的多属性建筑数据集CMAB。通过地理空间人工智能框架，利用高分辨率的Google Earth影像和街景图像，构建了包含楼顶、高度、功能、质量和年龄等属性的数据集，旨在为城市规划和可持续发展提供支持。

CMAB：首个基于开源数据和GeoAI的大规模多属性建筑数据集