Aerial image geolocalization from recognition and matching of roads and intersections

TL;DR

This paper presents a novel pipeline for automatic geolocalization of aerial images by recognizing and matching roads and intersections, enabling location determination without GPS data, even in challenging conditions.

Contribution

The authors introduce a complete geolocalization pipeline that detects roads and intersections, matches them to labeled data, and aligns maps, advancing aerial image analysis capabilities.

Findings

High localization accuracy across different cities

Effective matching despite poor image quality

Improved road detection through alignment

Abstract

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.