Urban research is progressively moving towards fine-grained simulation and requires more
granular and accurate geospatial data. In comparison to building footprints, roof structure …

The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light
Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain …

This paper suggests a new algorithm for automatic building point cloud filtering based on the
Z coordinate histogram. This operation aims to select the roof class points from the building …

This article suggests a new approach to automatic building footprint modeling using
exclusively airborne LiDAR data. The first part of the suggested approach is the filtering of …

The identification of roof surfaces is characterized by a sequence of several processing
steps. The boundary detection of different types of roof is realized from light detection and …

This paper studies the fidelity level of the extracted LiDAR (Light Detection And Ranging)
building point cloud in relation to the original building. In this context, the building point …

Nowadays, many of the world's large cities are faced with the issue of land scarcity for
construction due to the increasing growth of urbanization, as well as the economic downturn …

Despite the large number of studies conducted during the last three decades concerning 3D
building modelling starting from Light detection and ranging (Lidar) data, two persistent …

In urban areas, a Light Detection And Ranging (LiDAR) point cloud contain four principal
classes: terrain, buildings, vegetation, and remained points. Each class has its own …

摘要建筑物屋顶平面形状各异且分布不均匀, 如何有效地实现机载点云屋顶平面的精细化分割已
成为建筑物三维重建中的关键问题之一. 为此, 本课题组提出了一种体素与点混合增长的机载点 …