The evolution of mobile mapping systems (MMSs) has gained more attention in the past few
decades. MMSs have been widely used to provide valuable assets in different applications …

Abstract 3D scene analysis in terms of automatically assigning 3D points a respective
semantic label has become a topic of great importance in photogrammetry, remote sensing …

We describe an effective and efficient method for point-wise semantic classification of 3D
point clouds. The method can handle unstructured and inhomogeneous point clouds such …

Machine learning techniques have gained a distinguished position in the automatic
processing of Light Detection and Ranging (LiDAR) data area. They represent the actual …

Point clouds collected in urban scenes contain a huge number of points (eg, billions),
numerous objects with significant size variability, complex and incomplete structures, and …

The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry,
remote sensing and computer vision. In this paper, we propose a new methodology for the …

Up-to-date catalogs of the urban tree population are of importance for municipalities to
monitor and improve quality of life in cities. Despite much research on automation of tree …

Segmentation of mobile laser point clouds of urban scenes into objects is an important step
for post-processing (eg, interpretation) of point clouds. Point clouds of urban scenes contain …

We propose a new methodology for large-scale urban 3D scene analysis in terms of
automatically assigning 3D points the respective semantic labels. The methodology focuses …

Automatic methods are needed to efficiently process the large point clouds collected using a
mobile laser scanning (MLS) system for surveying applications. Machine-learning-based …