Creating Roadmaps in Aerial Images with Generative Adversarial Networks and Smoothing-Based Optimization

Abstract

Recognizing roads and intersections in aerial images is a challenging problem in computer vision with many real world applications, such as localization and navigation for unmanned aerial vehicles (UAVs). The problem is currently gaining momentum in computer vision and is still far from being solved. While recent approaches have greatly improved due to the advances in deep learning, they provide only pixel-level semantic segmentations. In this paper, we argue that roads and intersections should be recognized at the higher semantic level of road graphs - with roads being edges that connect nodes. Towards this goal we present a method consisting of two stages. During the first stage, we detect roads and intersections with a novel, dual-hop generative adversarial network (DH-GAN) that segments images at the level of pixels. At the second stage, given the pixelwise road segmentation, we find its best covering road graph by applying a smoothing-based graph optimization procedure. Our approach is able to outperform recent published methods and baselines on a large dataset with European roads.