Automatic Detection of Changes from Laser Scanner and Aerial Image Data for Updating Building Maps

The goal of our study was to develop an automatic change detection method based on laser scanner, aerial image and map data to be used in updating of building maps. The method was tested in a study area of 2.2 km near Helsinki. Buildings were first detected by segmenting a digital surface model (DSM) derived from laser scanner data and classifying the segments as buildings, trees and ground surface. Height information, aerial image data, shape and size of the segments and neighbourhood information were used in classification. Detected buildings were then compared with an old building map and classified as new, enlarged and old buildings. Similarly, buildings of the old map were compared with the building detection result and classified as detected, partly detected and not detected. Compared with an up-to-date reference map, 88% of all buildings in the study area and 98% of buildings larger than 200 m were correctly detected in the building detection stage. Promising results were also obtained in change detection between the old map and the building detection result, especially in detecting new buildings. Results of the study suggest that automatic building detection and change detection is possible and could produce useful results for map updating. Further research should include improvement of the segmentation stage to better distinguish buildings from trees and development of the change detection method.