Identifying safe intersection design through unsupervised feature extraction from satellite imagery

@article{Wijnands2021IdentifyingSI,
  title={Identifying safe intersection design through unsupervised feature extraction from satellite imagery},
  author={Jasper S. Wijnands and Haifeng Zhao and Kerry A. Nice and Jason Thompson and Katherine Scully and Jingqiu Guo and Mark R. Stevenson},
  journal={Comput. Aided Civ. Infrastructure Eng.},
  year={2021},
  volume={36},
  pages={346-361}
}
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… Expand
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References

SHOWING 1-10 OF 88 REFERENCES
Combining Satellite Imagery and Open Data to Map Road Safety
TLDR
A deep learning-based mapping approach that leverages open data to learn from raw satellite imagery robust deep models able to predict accurate city-scale road safety maps at an affordable cost is proposed. Expand
Street smarts: measuring intercity road quality using deep learning on satellite imagery
TLDR
A model for monitoring the quality of road infrastructure using satellite imagery is developed, enabling much larger scale and much lower costs than are achievable with current methods, and providing insight into the contribution of high-quality roads to many societal development indicators. Expand
Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety
TLDR
This research uses publicly available big data such as maps and satellite images to capture information of the environment of cyclist crashes and suggests a deep learning method is able to learn the hidden features from map andatellite images and model the road environments using GANs. Expand
Feasibility of Computer Vision-Based Safety Evaluations
Traditional road safety analysis has often been undertaken with historical collision records. However, limitations on the quality and completeness of collision data gave rise to surrogate ways of… Expand
Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints
TLDR
An automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree and introduces a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree. Expand
Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine
Extracting road networks from very-high-resolution (VHR) aerial and satellite imagery has been a long-standing problem. In this article, a neural-dynamic tracking framework is proposed to extract… Expand
At the cross-roads: an on-road examination of driving errors at intersections.
  • Kristie L Young, P. Salmon, M. Lenné
  • Engineering, Medicine
  • Accident; analysis and prevention
  • 2013
TLDR
Traffic signal configuration was found to play a key role in intersection error causation, with drivers making more errors at partially signalised compared to fully signalised intersections. Expand
Use of Satellite Imagery for Establishing Road Horizontal Alignments
TLDR
A new method for establishing road horizontal alignment using IKONOS 1 m spatial resolution imagery is presented, which performs the search procedures in a much smaller area than the image size and achieves faster computations. Expand
Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks
TLDR
It is shown how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. Expand
Recent progress in road and lane detection: a survey
TLDR
This paper presents a generic break down of the problem of road or lane perception into its functional building blocks and elaborate the wide range of proposed methods within this scheme. Expand
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