Accurate facade parsing based on new facade dataset

Facade parsing is a vital technology for applications such as urban modeling, urban planning, and digital twin city construction. High-resolution facade images are particularly important for achieving fine-grained building reconstruction. However, existing facade datasets rarely contain images with resolutions exceeding 2K × 2K. In this paper, we introduce a new dataset featuring much higher-resolution images captured from various angles and with denser window distributions. We also propose a novel method to automatically compute the perspective transformation matrix for generating corrected facade images, which is used to create a twin version of the dataset after perspective correction. Furthermore, we introduce a new network GLNet, designed to achieve superior facade parsing results using high-resolution images as input. Experimental results on three public datasets (CMP, CFP, and ETRIMS) as well as our own dataset demonstrate that GLNet outperform existing methods in facade segmentation. The dataset and code are available at: https://github.com/OctAne0113/GLNet .