Building change detection from multitemporal airborne LiDAR data: assessment of different approaches

Abstract

Building change detection is an important task in urban applications. In the last decades, airborne LiDAR data have been explored, aiming at automatic or semi-automatic detection of different entities or objects. We show a comparative analysis considering three methods: two previously developed by the authors (called M1 and M2) and another based on the results derived from an open-source software (M3). The first developed method (M1) explores the height entropy concept and is based on threshold empirically determined to separate building and vegetation changes. The second method (M2) considers the planarity attribute and the Otsu algorithm to automatically separate the classes. The main purpose is to highlight differences among the methods, as well as discuss advantages and disadvantages considering a real scene, in which buildings with at least 20 m2 were considered. To perform the comparative analysis, qualitative and quantitative evaluation were conducted considering a study area located in the city of Presidente Prudente, Brazil. In the experiments, two airborne LiDAR datasets were used, acquired in 2012 and 2014. The results indicate the potential of methods M1 and M2, presenting Fscore around 80% and 83%, respectively. In contrast, the method M3 presented a Fscore around 50%.