Leveraging ontology for enhanced queries and analyses of urban point clouds

Abstract

Point clouds are widely used in domains such as urban planning, heritage conservation, and forestry. They often present challenges related to processing, semantic enrichment, and querying due to their large size and complexity. This paper introduces a general ontology-based approach, embedded into a tool named 3Dont, that enhances the semantic structure and usability of point clouds across various fields. By representing the individual points of a clouds within an ontology, we enable easy access to dynamic, semantically rich, and highly queryable datasets that integrate multi-source and multi-temporal data. This methodology provides a spatially consistent and user-friendly representation, allowing for intuitive exploration and analysis through ontology-based queries. The approach facilitates data interoperability and high-level feature extraction, offering a versatile tool for diverse 3D data applications. A video showcasing the capabilities of the 3Dont tool is available at https://www.youtube.com/watch?v=Nvg2E755JNg.