Change Detection of Building Footprints from Airborne Laser Scanning Acquired in Short Time Intervals

Several recent studies have shown that airborne laser scanning (ALS) of urban areas delivers valuable information for 3D city modelling and map updating. Building footprint detection from multi-temporal ALS lacks in comparability because of changing ALS flight parameters, flying season, interpolation settings if digital elevation models are used, and the ability of the used building detection method to deal with these influences. So far, less attention has been paid to change detection of buildings within a short time span (approx. three months), where major problems are the high variability of vegetation over time and to distinguish temporary objects from small changes of buildings, which are currently under construction and demolition, respectively. We introduce an object-based workflow to investigate how unchanged objects can be defined, which variability in the object appearance is allowed to define an object as unchanged, and at which threshold a change can be indicated. The test site is situated in the city of Innsbruck (Austria) where ALS data is available from summer and autumn in 2005. In an initial step building footprints are derived by an object-based image analysis (OBIA) detection method for each flight independently. The parameters for building detection are derived for a training site in order to automatically derive the rules of the classification tree. Then the object features of buildings derived from the different flights are compared to each other and separated into the classes unchanged building, new building, demolished building, new building part, and demolished building part. The results are verified by a reference, which was created manually by visual inspection of the elevation difference image of both epochs. For new buildings and building parts 90% and for demolished buildings and building parts 32% were detected correctly. The detection of demolished buildings is strongly influenced by the appearance of high vegetation, which is caused by the decreasing heights of trees by comparing summer (leaf-on) and autumn (leaf-off) ALS data. * Corresponding author.