M. Previtali, D. Treccani, M. Garramone, Andrea Adami, M. Scaioni
2026.2.20Applied Geomatics
tlooto Summary
Testing and comparing different existing ML/DL frameworks for the classification of the point cloud of a historic urban area using two open-source and two commercial software packages was selected, based on the results of the literature review.
Abstract
In the digital AECO (Architecture, Engineering, Construction & Operations) domain, 3D point clouds are, nowadays, an increasingly used data source to derive geometric and semantic information. The fast data collection and completeness as well as the direct digital output are the main advantages with respect to traditional 3D acquisition
Citation format
PREVITALI, M., et al. Advancements in portable mobile mapping system point-cloud classification: A user-centric comparison of open-source and commercial machine learning and deep learning solutions. Applied Geomatics, 2026, 18.