Abstract
The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape. Laser scanning technology (LiDAR - Light Detection and Ranging) enables such approach producing dense 3-D point clouds of millions of points, which, however, contain noise. Therefore, we developed a new approach allowing for a semi-automatic elimination of data noise resulting from motion of objects within the scanned scene such as persons. We used a connected-component labelling method to filter out the noise points from terrestrial laser scanning point clouds. Our approach was based on a step-by-step object classification with a proper parameterisation. In the first step, all points located close to the predicted terrain were selected. In the second step, the points representing the terrain and floor were classified using the surface filter tool implemented in the RiScan Pro software by RIEGL. The rest of points were classified using point cloud clustering via the connected-component labelling method implemented in the CloudCompare software. In the final step, the operator manually decides whether the point cluster represents the noise. The method was applied to the Cathedral of Saint Elizabeth, a sacral object located in the historical centre of the city of Košice in Slovakia during normal operating hours. We managed to capture approximately 80% of the data noise in total. The method provides a better flexibility in surveying overcrowded city locations using the laser scanning technology.
Highlights
The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape
We focus on evaluating the classification of noise in the LiDAR point clouds by a combination of label connected components (LCC) filtering and manual classification
The second section focuses on the quality of integration of point clouds from multiple sources, i.e. airborne and terrestrial laser scanning, to generate a 3D model of the cathedral
Summary
The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape. The Cathedral of St. Elizabeth located in the historical centre of the city of Košice (Fig. 1) was selected for the demonstrating the process of semi-automatic classification of point cloud from laser scanning using the connected component labelling method. We performed the classification of TLS point clouds in several steps separately for point clouds representing the cathedral exterior and interior (Fig. 3, P5, Fig. 6).
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