Journal article
Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 3.0MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-023-38100-1
Authors
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 13
- Issue:
- 1
- Article number:
- 10922
- Publication date:
- 2023-07-05
- Acceptance date:
- 2023-07-03
- DOI:
- EISSN:
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2045-2322
- Pmid:
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37407750
- Language:
-
English
- Pubs id:
-
1568940
- Local pid:
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pubs:1568940
- Deposit date:
-
2024-03-08
Terms of use
- Copyright holder:
- Francis et al.
- Copyright date:
- 2023
- Rights statement:
- Copyright © 2023, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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