Mapping road safety features from streetview imagery: A deep learning approach

TL;DR

This paper presents a deep learning method combining CNNs and LSTMs to automatically map road safety features from streetview images, improving efficiency over manual methods for transportation safety management.

Contribution

It introduces a novel deep learning model that integrates spatial context via LSTM with CNNs for accurate mapping of road safety features from imagery.

Findings

Model outperforms baseline methods in accuracy.

Effective use of spatial autocorrelation improves mapping.

Automates a traditionally manual, costly process.

Abstract

Each year, around 6 million car accidents occur in the U.S. on average. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for federal or state transportation agencies, helping traffic engineers identify locations to invest on safety infrastructure. In current practice, mapping road safety features is largely done manually (e.g., observations on the road or visual interpretation of streetview imagery), which is both expensive and time consuming. In this paper, we propose a deep learning approach to automatically map road safety features from streetview imagery. Unlike existing Convolutional Neural Networks (CNNs) that classify each image individually, we propose to further add Recurrent Neural Network (Long Short Term Memory) to capture geographic context of images (spatial autocorrelation effect along linear road network paths). Evaluations on real world streetview imagery show that our proposed model outperforms several baseline methods.