Identifying safe intersection design through unsupervised feature extraction from satellite imagery
@article{Wijnands2020IdentifyingSI, title={Identifying safe intersection design through unsupervised feature extraction from satellite imagery}, author={Jasper S. Wijnands and Haifeng Zhao and Kerry A. Nice and J. Thompson and Katherine Scully and Jingqiu Guo and M. Stevenson}, journal={ArXiv}, year={2020}, volume={abs/2010.15343} }
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… CONTINUE READING
References
SHOWING 1-10 OF 76 REFERENCES
Street smarts: measuring intercity road quality using deep learning on satellite imagery
- Computer Science
- COMPASS
- 2019
- 7
- PDF
Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety
- Computer Science
- 2019
- 2
Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints
- Computer Science
- IEEE Transactions on Geoscience and Remote Sensing
- 2007
- 279
- PDF
Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine
- Engineering
- 2015
- 91
At the cross-roads: an on-road examination of driving errors at intersections.
- Engineering, Medicine
- Accident; analysis and prevention
- 2013
- 21
Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks
- Computer Science, Mathematics
- Neural Computing and Applications
- 2019
- 9
- PDF
Recent progress in road and lane detection: a survey
- Computer Science
- Machine Vision and Applications
- 2011
- 538
- PDF