POINT CLOUD CLASSIFICATION OF AN URBAN ENVIRONMENT USING A SEMI-AUTOMATIC APPROACH

In this contribution a point cloud classification in an urban context has been presented. The aim of the work is to test a semiautomatic classification approach and to verify its usefulness in the scan-to BIM process, and to validate how much it is straightforward for the definition of different point cloud LODs. The work methodology is structured in three phases. The first concerns data acquisition and processing through geomatic instruments and methodologies that guarantee a complete and expeditious survey such as ground-based MMS and UAV for aerial photogrammetry. The second phase concerns the testing of an online software that performs point cloud classifications through AI algorithms. The system allows either to use standard classifiers that are already available, or to create a customizable catalogue of the different classes that one wants to attribute to the urban scene. Following the automatic classification process, where all objects have been identified, manual corrections can be made to improve the classification of objects into specific classes. The third step is object detection and extraction. Here, the relationship between automatic classification, point cloud density, object identification and the various degrees of LOD definition was explored. The higher the LOD, the greater the number of objects that can be identified, particularly those elements related to street furniture and urban facilities. Once these objects have been classified, it is then possible to extract them in interoperable format. This allows such data to be managed and shared through BIM platform