This study aims at the robust automatic detection of buildings with a gable roof in varying rural areas from
very-high-resolution aerial images. The originality of our approach resides in a custom-made design extracting
key features close to modeling, such as e.g. roof ridges and gutters. In this way, we allow a large freedom in
roof appearances. The proposed method is based on a combination of two hypotheses. First, it exploits the
physical properties of gable roofs and detects straight line-segments within non-vegetated and non-farmland
areas, as possibilities of occurring roof-ridges. Second, for each of these candidate roof-ridges, the likely roof-gutter
positions are estimated for both sides of the line segment, resulting in a set of possible roof configurations.
These hypotheses are validated based on the analysis of size, shadow, color and edge information, where for
each roof-ridge candidate the optimal configuration is selected. Roof configurations with unlikely properties are
rejected and afterwards ridges with overlapping configurations are fused. Experiments conducted on a set of 200
images covering various rural regions, with a large variation in both building appearance and surroundings, show
that the algorithm is able to detect 75% of the buildings with a precision of 69.4%. We consider this as a
reasonably good result, since the computing is fully unconstrained, numerous buildings were occluded by trees
and because there is a significant appearance difference between the considered test images.