# Attentional Road Safety Networks

## TL;DR

This paper introduces a region guided attention network with domain adaptation for road safety classification from satellite images, achieving high accuracy and outperforming subjective analysis.

## Contribution

It proposes a novel region guided attention network combined with a domain adaptation approach and a covariance-based loss function for improved road safety mapping.

## Key findings

- Achieves 86.21% accuracy on London dataset.
- Increases accuracy by 4% on NYC with domain adaptation.
- Outperforms subjective analysis by 23.12%.

## Abstract

Road safety mapping using satellite images is a cost-effective but a challenging problem for smart city planning. The scarcity of labeled data, misalignment and ambiguity makes it hard for supervised deep networks to learn efficient embeddings in order to classify between safe and dangerous road segments. In this paper, we address the challenges using a region guided attention network. In our model, we extract global features from a base network and augment it with local features obtained using the region guided attention network. In addition, we perform domain adaptation for unlabeled target data. In order to bridge the gap between safe samples and dangerous samples from source and target respectively, we propose a loss function based on within and between class covariance matrices. We conduct experiments on a public dataset of London to show that the algorithm achieves significant results with the classification accuracy of 86.21%. We obtain an increase of 4% accuracy for NYC using domain adaptation network. Besides, we perform a user study and demonstrate that our proposed algorithm achieves 23.12% better accuracy compared to subjective analysis.