In this work, we propose a geometry-aware semi-supervised method for fine-grained building function recognition. This method leverages the geometric relationships between multi-source data to improve the accuracy of pseudo labels in semi-supervised learning, extending the task's scope and making it applicable to cross-categorization systems of building function recognition. Firstly, we design an online semi-supervised pre-training stage, which facilitates the precise acquisition of building facade location information in street-view images. In the second stage, we propose a geometry-aware coarse annotation generation module. This module effectively combines GIS data and street-view data based on the geometric relationships, improving the accuracy of pseudo annotations. In the third stage, we combine the newly generated coarse annotations with the existing labeled dataset to achieve fine-grained functional recognition of buildings across multiple cities at a large scale. Extensive experiments demonstrate that our proposed framework exhibits superior performance in fine-grained functional recognition of buildings. Within the same categorization system, it achieves improvements of 7.6% and 4.8% compared to fully-supervised methods and state-of-the-art semi-supervised methods, respectively. Additionally, our method also performs well in cross-city tasks, i.e., extending the model trained on OmniCity (New York) to new areas (i.e., Los Angeles and Boston). This study provides a novel solution for the fine-grained function recognition of large-scale buildings across multiple cities, offering essential data for understanding urban infrastructure planning, human activity patterns, and the interactions between humans and buildings.

本研究提出了一种几何感知的半监督方法以进行精细化建筑功能识别，解决了传统方法在伪标签精度上的不足。通过设计一种在线的半监督预训练阶段，并结合GIS数据和街景数据生成高精度的粗略注释，实现了跨城市的大规模建筑功能识别。实验结果表明，该方法在多个城市的精细化功能识别中优于现有的监督和半监督方法，具有显著的应用潜力。 

基于几何感知的半监督学习进行街景图像精细化建筑功能识别