Curbside management from MLS and HMLS point clouds to CityGML 3.0

It is of significant importance to ensure the secure and efficient movement of both pedestrians and vehicles in order to develop an accessible urban environment. This research aims to generate a CityGML model from point clouds, considering the use of curbsides by both pedestrians and vehicles. Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds from three different cities were employed for the automatic classification of curbside areas, including parking for various users, parking by time, parking entrances, garbage bin spaces, and terrace areas. Subsequently, the point clouds were processed for modeling in accordance with the international OGC standard, CityGML version 3.0. The findings demonstrate an overall accuracy of 0.86 in correctly classifying curbside elements in comparison to ground truth data. Furthermore, the point-to-point analysis indicated an F1-score exceeding 0.8 across categories and an IoU mean of 0.8, which serves to underscore the effectiveness of the method. This approach directly generates a semantic 3D streetspace model of the curbside in CityGML from point clouds, ensuring standardized and interoperable access to the data.     «

It is of significant importance to ensure the secure and efficient movement of both pedestrians and vehicles in order to develop an accessible urban environment. This research aims to generate a CityGML model from point clouds, considering the use of curbsides by both pedestrians and vehicles. Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds from three different cities were employed for the automatic classification of curbside areas, including parking for variou...     »