Identifying safe intersection design through unsupervised feature extraction from satellite imagery

JS Wijnands; H Zhao; KA Nice; J Thompson; K Scully; J Guo; M Stevenson

Journal article

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

JS Wijnands, H Zhao, KA Nice, J Thompson, K Scully, J Guo, M Stevenson

Computer Aided Civil and Infrastructure Engineering | WILEY | Published : 2021

DOI: 10.1111/mice.12623

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Abstract

The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. This article presents the first study to systematically analyze the design of all intersections in a large country, based on aerial imagery and deep learning. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high-level features, including the intersection's type, size, shape, lane markings, and complexity, which were used to cluster similar designs. An Australian telematics data set linked infrastructure design to driving..

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University of Melbourne Researchers

Kerry Nice Author

Jason Thompson Author

Mark Stevenson Author

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Grants

Awarded by National Health and Medical Research Council

Funding Acknowledgements

ACT Road Safety Fund, Grant/Award Number: 17/8281; LIEFHPC-GPGPU Facility, Grant/Award Number: LE170100200; AustralianResearch Council Discovery Early Career Researcher Award, Grant/Award Number: DE180101411; National Health and Medical Research Council, Grant/Award Number: APP1136250