Unsupervised feature extraction and clustering of aerial images for understanding hazardous road segments

Abstract

Satellite and aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. Through the clustering of hazardous road segments with these latent image features, this work demonstrates how aerial images and machine learning can provide a data-driven aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur.