Detection of Man-Made Constructions Using LiDAR Data and Decision Trees

Abstract

Real estate monitoring is very important aspect of country economics, but old manual methods of land survey are time and resources consuming processes as geodata actualization tasks. Actual, precise, multidimensional and detailed information is the main instrument of geospatial intelligence to understand current economic situation and to make effective decision. Actualization of geoinformation using remote sensing is the modern approach of the computer age to complete Earth observation and human environment monitoring. This article describes multi-stage classification model, which detects man-made constructions in LiDAR point cloud. Proposed classification model applies decision tree and geometrical features of shape to remove noises. The goal of study is to experimentally compare decision trees with crisp and fuzzy logic (ID3 algorithms) to select the more suitable algorithm for noise reduction task. Algorithms are compared using total accuracy and Cohen’s Kappa coefficient.