Leroux, F.; Germain, M.; Clabaut, É.; Bouroubi, Y.; St-Pierre, T. Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction. ISPRS Int. J. Geo-Inf.2024, 13, 20.
Leroux, F.; Germain, M.; Clabaut, É.; Bouroubi, Y.; St-Pierre, T. Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction. ISPRS Int. J. Geo-Inf. 2024, 13, 20.
Leroux, F.; Germain, M.; Clabaut, É.; Bouroubi, Y.; St-Pierre, T. Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction. ISPRS Int. J. Geo-Inf.2024, 13, 20.
Leroux, F.; Germain, M.; Clabaut, É.; Bouroubi, Y.; St-Pierre, T. Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction. ISPRS Int. J. Geo-Inf. 2024, 13, 20.
Abstract
Digital twins are gaining in popularity for simulating complex natural and urban environments. In this context, accurate segmentation of objects within 3D urban environments is of crucial importance. The aim of this project is to develop a methodology for extracting buildings from textured 3D meshes. To this end, PicassoNet-II, a semantic segmentation architecture is employed. The methodology also incorporates Markov field-based contextual analysis to assess post-segmentation features. In addition, building instantiation is performed using cluster analysis algorithms. Training this model to fit various datasets requires a large amount of annotated data, both from Quebec City, Canada, and from simulated data. Experimental results show that the use of simulated data improves segmentation accuracy, and the DBScan algorithm proves effective in extracting isolated buildings. This project paves the way for improved applications in 3D urban modeling, offering opportunities in fields ranging from urban planning to resource management. The positive results with simulated data reinforce the impact of this research on improving digital models of our ever-changing urban environments.
Keywords
semantic segmentation; 3D building segmentation; cluster analysis; 3D city models; 3D mesh; data simulation; contextual analysis; markov random fields
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
