Aerial image geolocalization from recognition and matching of roads and intersections

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, environment monitoring or disaster relief. The problem is enjoying a great interest in computer vision and remote sensing, due to the increasing computational power and improvements 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 where 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. This step is 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, with relatively poor quality of the aerial images. We also show that the alignment between detected roads and pre-stored manual annotations can be effectively used for improving the quality of road detection.