The Impact of Optimizing the Number of Points of ALS Data Set on the Accuracy of the Generated DTM

Abstract

Airborne laser scanning technology delivers the result of the survey in the form of a point cloud. In order to construct a digital terrain model, it is necessary to perform filtration, which consists in separating data reflecting the relief features from the data reflecting situational details. In view of the very large amount of data in the survey data set, as well as the time consumption and difficulty in automatic filtration of the point cloud, it is possible to apply an optimization algorithm reducing the size of the point cloud while deriving a digital terrain model. This study presents the stages of compiling an airborne laser scanning point cloud using filtration and optimization. The filtration was carried out using the adaptive TIN model and the method of robust moving surfaces, while optimization was carried out with the application of an already existing algorithm to reduce the size of the survey data set. The effect of reducing the size of the data set on the accuracy of the generated DTM was tested and empirical and numerical tests have been performed