LEICA BLK2GO POINT CLOUD CLASSIFICATION WITH MACHINE LEARNING ALGORITHMS: THE CASE STUDY OF SANT’EUSEBIO’S CRYPT IN PAVIA (ITALY)

Abstract. Valorisation of cultural heritage is a priority of international community, and the creation of 3D models is considered almost a mandatory requirement to any conservation activity. Geomatics has given its contribution to this purpose offering its competences and experiences in surveying. Methods and tools have progressively improved offering more realistic, accurate, and reliable products; among the most interesting systems currently available, there is the handheld mobile mapping Leica BLK2GO technology. The characteristics of this system are particularly useful when traditional methods are unfeasible due to accessibility problem, narrowness spaces and time constraints. Nevertheless, the system, as any new technology, needs to be tested in order to verify its capability to describe the world in a correct and reliable way; moreover, it is interesting to understand if established data classification procedures are still effective for SLAM (Simultaneous Localization and Mapping) data. The paper is framed in this context and illustrates the survey of an ancient crypt, located in Pavia (Italy), that is object of a preservation project. The characteristics of the monument and the acquisition strategies are described. For data classification three different Machine Learning approaches are test: Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF). Seven architectural elements are considered: pavement, columns, half pilasters with structural function, walls, capitals, arches, and vaults. The analysis shows as Leica BLK2GO data owns all the characteristics feasible to produce useful point cloud; data classification performs well (with the exceptions of SVM) with higher accuracy, of about 90%, reached using RF.