In this paper, we improve upon the automatic building extraction method, which uses a variational inference Gaussian mixture model for performing color clustering, by accelerating its computational speed. The improved method decreases the computational time using an image with reduced resolution upon applying color clustering. According to our experiment, in which we used 106 scenery images, the improved method could extract buildings at a rate 86.54% faster than that of the conventional methods. Furthermore, the improved method significantly increased the extraction accuracy by 1.8% or more by preventing over-clustering using the reduced image, which also had a reduced number of the colors.

We have developed a map-based approach that enables us to efficiently extract information about man-made objects, such as buildings, from aerial images. An image is matched with a corresponding map in order to estimate the object information in the image (i. e. , presence, location, shape, size, kind, and surroundings). This approach is characterized by using a figure contained in a map as an object model for a top-down (model-driven) analysis of an object in the aerial image. We determined the principal steps of the map-based approach needed to extract object information and update a map. These steps were then applied to obtain the locations of missing buildings and the heights of existing buildings. The extraction results of experiments using aerial images of Kobe City (taken after the 1995 earthquake) show that the approach is effective for automatically extracting building information from aerial images and for rapidly updating map data.