Automated Updating of Building Data Bases from Digital Surface Models and Multi-spectral Images: Potential and Limitations

A method for automatic updating of building data bases from Digital Surface Models (DSM) and a normalised difference vegetation index is evaluated. The DSM can be generated from Airborne Laserscanner (ALS) data or by image matching techniques. Buildings are detected automatically from the input data. The building detection results are compared to an existing building data base, and changes between the existing data base and the new data set are determined. Buildings and building parts are classified as being confirmed, changed, new, or demolished. Change detection considers the fact that the original data and the building detection results can have a different topology and that small differences between the data from the two epochs might be caused by generalisation errors, by a misalignment of the data, or by insufficient sensor resolution. The performance of the algorithm is analysed using DSMs generated both from ALS data and by image matching. The evaluation shows the different properties of these data for building change detection and also some of the limitations of the method. If the accuracy requirements for the building outlines are not very high, the automatic updating process can be automated, provided that high-quality DSMs are used. In a semi-automatic environment the amount of human interaction for updating building data bases can be reduced by 40%-60%.