A study of integration methods of aerial imagery and LIDAR data for high level of automation in 3D building reconstruction

This paper describes integration methods to increase the level of automation in building reconstruction. Aerial imagery has been used as a major source in mapping fields and, in recent years, LIDAR data became popular as another type of mapping resources. Regarding to their performances, aerial imagery has abilities to delineate object boundaries but leaves many missing parts of boundaries during feature extraction. LIDAR data provide direct information about heights of object surfaces but have limitation for boundary localization. Efficient methods using complementary characteristics of two sensors are described to generate hypotheses of building boundaries and localize the object features. Tree structures for grid contours of LIDAR data are used for interpretation of contours. Buildings are recognized by analyzing the contour trees and modeled with surface patches from LIDAR data. Hypotheses of building models are generated as combination of wing models and verified by assessing the consistency between the corresponding data sets. Experiments using aerial imagery and laser data are presented. Our approach shows that the building boundaries are successfully recognized through our contour analysis approach and the inference from contours and our modeling method using wing model increase the level of automation in hypothesis generation/verification steps.