Building Detection and Reconstruction from Aerial Images

This paper presents a new method for building detection and reconstruction from aerial images. In our approach, we extract the useful building location information from the generated disparity map to segment the interested objects and consequently reduce unnecessary line segments extracted in low level feature extraction step. Hypothesis selection is carried out by using undirected graph, in which close cycles represent complete rooftops hypotheses. By using undirected graph, hypothesis selection becomes a simple graph search for close cycles. This significantly improves the performance of the system over the traditional hypothesis selection methods. We test the proposed method with the synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that our method can be efficiently used for the task of building detection and reconstruction from aerial images. * Corresponding author.