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DeepRoadMapper: Extracting Road Topology From Aerial Images

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Abstract

Creating road maps is essential for applications such as autonomous driving and city planning. Most approaches in industry focus on leveraging expensive sensors mounted on top of a fleet of cars. This results in very accurate estimates when exploiting a user in the loop. However, these solutions are very expensive and have small coverage. In contrast, in this paper we propose an approach that directly estimates road topology from aerial images. This provides us with an affordable solution with large coverage. Towards this goal, we take advantage of the latest developments in deep learning to have an initial segmentation of the aerial images. We then propose an algorithm that reasons about missing connections in the extracted road topology as a shortest path problem that can be solved efficiently. We demonstrate the effectiveness of our approach in the challenging TorontoCity dataset and show very significant improvements over the state-of-the-art.

Authors

Gellert Máttyus, Wenjie Luo, Raquel Urtasun

Conference

ICCV 2017

Full Paper

‘DeepRoadMapper: Extracting Road Topology From Aerial Images’ (PDF)

Uber ATG

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Gellert Mattyus is a research scientist at Uber ATG Toronto working on computer vision and machine learning problems related to self-driving with an emphasis on perceiving maps. Gellert Mattyus has earned his PhD at the Remote Sensing Technology Chair of the Technical University of Munich (TUM) while working as a research scientist at the Photogrammetry and Image Analysis Department of the German Aerospace Center (DLR). After earning his PhD, Gellert Mattyus has spent nearly a year as a post-doc at the Machine Learning Group of the University of Toronto under the supervision of Professor Raquel Urtasun.