Automatic delineation of buildings is very attractive for both civilian and military applications. Such applications include general mapping, detection of unauthorized constructions, change detection, etc. For military applications, high demand exists for accurate building change updates, covering large areas, and over short time periods. We present two algorithms coupled together. The
height image algorithm is a fast coarse algorithm operating on large areas. This algorithm is capable of defining blocks of buildings and regions of interest. The point-cloud algorithm is a fine, 3D-based, accurate algorithm for building delineation. Since buildings may be
separated by alleys, whose width is similar or narrower than the LADAR resolution, the height image algorithm marks those crowded buildings as a single object. The point-cloud algorithm separates and accurately delineates individual building boundaries and building sub-sections utilizing roof shape analysis in 3D. Our focus is on the ability to cover large areas with accuracy and high rejection of non-building objects, like trees. We report a very good detection performance with only few misses and false alarms. It is believed that LADAR measurements, coupled with good segmentation algorithms, may replace older systems and methods that require considerable manual work for such applications.