  Aerial image analysis at a semantic level is important in many applications
with strong potential impact in industry and consumer use, such as automated
mapping, urban planning, real estate and environment monitoring, or disaster
relief. The problem is enjoying a great interest in computer vision and remote
sensing, due to increased computer power and improvement in automated image
understanding algorithms. In this paper we address the task of automatic
geolocalization of aerial images from recognition and matching of roads and
intersections. Our proposed method is a novel contribution in the literature
that could enable many applications of aerial image analysis when GPS data is
not available. We offer a complete pipeline for geolocalization, from the
detection of roads and intersections, to the identification of the enclosing
geographic region by matching detected intersections to previously learned
manually labeled ones, followed by accurate geometric alignment between the
detected roads and the manually labeled maps. We test on a novel dataset with
aerial images of two European cities and use the publicly available
OpenStreetMap project for collecting ground truth roads annotations. We show in
extensive experiments that our approach produces highly accurate localizations
in the challenging case when we train on images from one city and test on the
other and the quality of the aerial images is relatively poor. We also show
that the the alignment between detected roads and pre-stored manual annotations
can be effectively used for improving the quality of the road detection
results.
