SEMANTIC LABELING OF 3D BUILDINGS BY USING GRAPH NEURAL NETWORK (GNN)

Abstract

Understanding building semantics is crucial for comprehending their structure, components, and functions. This study investigates the utilization of Graph Neural Networks (GNN) to semantically label 3D building models, aiming to optimize these labels' output within Geographic Information Systems (GIS). The methodology leverages diverse datasets comprising 3D building models, utilizing BuildingGNN algorithms to iteratively refine the labeling process. The resultant labeled components, after thorough validation, are translated into the CityGML format. The generated CityGML dataset holds promise for a wide array of 3D city model applications, heightening the utility of labeled components within GIS analyses. While exploring GNN's capabilities may reduce manual effort and time and provide standardized data representation, this study also addresses challenges in ensuring output reliability. While automation remains a goal, future research endeavors may focus on refining automatic labeling techniques and semantic translation processes to further improve accuracy and applicability.