Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Abstract. Building change detection serves to investigate illegal buildings. Illegal built or removed structures, especially those concealed among gable roofs such as dormers, are difficult to track among potentially millions of buildings. Nevertheless, they can be efficiently located in changed areas. An approach is proposed to automatically detect and classify changes in buildings from two epochs of Airborne Laser Scanning Data. Both datasets are classified into water, ground, building, vegetation and undefined objects in advance. After generalization of a 3D surface separation map, we verify changes by making rules on the separation map. Changes belonging to buildings are then classified into roof, wall, dormers, vehicles, construction above roof and undefined objects. As the ALS data has accuracy in strip difference of lower than 5 cm within the same epoch and from different epochs, changes that are larger than 10 cm were detected. Building changes, which areas are larger than 4 m², are identified as change. By inspection, nearly all changes are detected and approximately 80% changes are correctly classified.