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

John Francis, Jonathan Bright, Saba Esnaashari, Youmna Hashem, Deborah Morgan, Vincent J. Straub

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

Abstract

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 with aerial imagery. 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. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur.

Original languageEnglish
Article number10922
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - 5 Jul 2023

Bibliographical note

Funding Information:
This work was supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1 & The Alan Turing Institute.

ASJC Scopus subject areas

  • General

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