Simple method of LiDAR point density definition for automatic building recognition

Aerial laser scanning is a modern and accurate remote sensing technology how to scan the earth’s surface and to get its digital surface model. The digital surface model is applied for different economical tasks.
The result of aerial laser scanning is 3D point cloud, which must be preprocessed before usage. There are three groups of preprocessing tasks: noise filtering, object recognition and generation of vector maps or 3D model.
This report is related with the object recognition field. The main parameter of aerial laser scanning is the point density, which is expressed as the point number per square meter. Therefore, it is important to know the minimal point density per square meter, which must be satisfied to recognize the object for stakeholders and the delivery of LiDAR data. The existing scientific publications only describe recognition methods, but they do not provide some precise method to chose the necessary point density for business needs. So, there is the need for some method, which can be used to define this minimal point density. This document provides the simple equation to calculate the minimal point density for building recognition. The equation is expressed from the analysis of the mathematical model. The analysis is based on the exploration of the object location patterns and probability to detect this object. The theoretical model is experimentally evaluated using high density LiDAR data, the point density minimization algorithm and the building recognition method.