Abstract
Updating digital map databases to reflect continuous changes is a major task, which could be significantly reduced by introducing an automated change-detection process. We suggest an algorithm inspired by the specific problem of updating the Danish National Topographic Map Database (TOP10DK). The emphasis is on buildings, which are very important mapping objects. The algorithm uses vector and spectral data as input to an unsupervised spectral classification method which controls a subsequent Mahalanobis classification step. For evaluation, we present four test cases based on the TOP10DK database in combination with RGB and color-infrared (CIR) aerial photos. A main problem is roofs covered with roofing felt, which are hard to discriminate spectrally from roads. Apart from that, RGB photos are barely sufficient for change detection, while CIR data give more satisfactory results. Refinements are, however, still needed to reduce the number of false alarms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
