Volunteer Geographic Information (VGI), with its rich variety, large volume, rapid updates, and diverse sources, has become a critical source of geospatial data. However, VGI data from platforms like OSM exhibit significant quality heterogeneity across different data types, particularly with urban building data. To address this, we propose a multi-source geographic data transformation solution, utilizing accessible and complete VGI data to assist in generating urban building footprint data. We also employ a multimodal data generation framework to improve accuracy. First, we introduce a pipeline for constructing an 'image-text-metadata-building footprint' dataset, primarily based on road network data and supplemented by other multimodal data. We then present ControlCity, a geographic data transformation method based on a multimodal diffusion model. This method first uses a pre-trained text-to-image model to align text, metadata, and building footprint data. An improved ControlNet further integrates road network and land-use imagery, producing refined building footprint data. Experiments across 22 global cities demonstrate that ControlCity successfully simulates real urban building patterns, achieving state-of-the-art performance. Specifically, our method achieves an average FID score of 50.94, reducing error by 71.01% compared to leading methods, and a MIoU score of 0.36, an improvement of 38.46%. Additionally, our model excels in tasks like urban morphology transfer, zero-shot city generation, and spatial data completeness assessment. In the zero-shot city task, our method accurately predicts and generates similar urban structures, demonstrating strong generalization. This study confirms the effectiveness of our approach in generating urban building footprint data and capturing complex city characteristics.

该研究解决了志愿地理信息（VGI）中城市建筑数据质量不均的问题，通过多源地理数据转化方案生成建筑轮廓数据。提出的ControlCity方法基于多模态扩散模型，利用文本-图像模型与改进的ControlNet整合数据，成功模拟真实城市建筑模式，展现了显著的准确性提升。

ControlCity：基于多模态扩散模型的方法用于精确的地理空间数据生成与城市形态分析