Detection of Buildings in Colour and Colour-infrared Aerial Photos for Semi-automated Revision of Topographic Databases

With the growing use of digital map data, the requirements for frequent and efficient revision of digital map databases is equally growing. Revision of map databases can be divided into 3 steps: Change detection, classification of changes and registration/updating of the thematic layers of the database. Often the change detection step is carried out in an entirely manual workflow, which calls for automation since it is tedious, labour intensive, and hence very costly. In this paper, we concentrate on automated change detection for the “building” layer in a fully 3D geo-spatial database. We present a method based on the use of unsupervised classification as supervising input to a Mahalanobis classification algorithm. The method is evaluated in test cases based on building registrations from the Danish TOP10DK map database, in combination with a plain RGB colour aerial photo and a colour infra red (CIR) aerial photo covering the same area. The test case presented here is from a suburban residential area. At the present stage, plain RGB aerial photos are not enough to significantly reduce the update task. CIR photos show more promise, as the change algorithm, in general, detects all new buildings, although it still needs refinements to reduce the number of false alarms. Buildings with flat asphalt roofs are an entirely separate problem, since, in the lack of height information, they are extremely difficult to discern from roads.