{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T07:46:21Z","timestamp":1768290381042,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rural roads play a crucial role in fostering economic and social development in Africa. Local Road Authorities (LRAs) struggle to collect road condition data using conventional means due to logistical and resource issues. Poor road conditions and restricted mobility have severe economic consequences for the transport of goods and services. Lack of maintenance can increase costs three-fold. In this work, a novel framework is proposed in which earth observations using high-resolution optical satellite imagery are applied to measure the condition of unpaved roads, providing a vital input to maintenance planning and prioritisation. A trial was conducted using this method on 83 roads in Tanzania totalling 131.7 km. The experimental results demonstrate that, by analysing variations in pixel intensity of the road surface, the condition can be estimated with an accuracy of 71.9% when compared to ground truth information. Machine Learning techniques are applied to the same network to test the performance of the system in predicting road conditions. A blended classifier approach achieves an accuracy of 88%. The proposed framework enables LRAs to define the information they receive based on their specific priorities, offering a rapid, objective, consistent and potentially cost-effective system that overcomes the current challenges faced by LRAs.<\/jats:p>","DOI":"10.3390\/rs15163985","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T10:33:23Z","timestamp":1691750003000},"page":"3985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of Unpaved Road Conditions Using High-Resolution Optical Satellite Imagery and Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0575-3565","authenticated-orcid":false,"given":"Robin","family":"Workman","sequence":"first","affiliation":[{"name":"TRL Ltd., Crowthorne House, Nine Mile Ride, Wokingham, Berks RG40 3GA, UK"}]},{"given":"Patrick","family":"Wong","sequence":"additional","affiliation":[{"name":"School of Computing & Communications, The Open University, Walton Hall, Kents Hill, Milton Keynes MK7 6AA, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8795-1313","authenticated-orcid":false,"given":"Alex","family":"Wright","sequence":"additional","affiliation":[{"name":"TRL Ltd., Crowthorne House, Nine Mile Ride, Wokingham, Berks RG40 3GA, UK"}]},{"given":"Zhao","family":"Wang","sequence":"additional","affiliation":[{"name":"TRL Ltd., Crowthorne House, Nine Mile Ride, Wokingham, Berks RG40 3GA, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","first-page":"70","article-title":"Rural Roads\u2013roles, challenges and solutions for Sub-Saharan Africa\u2019s sustainable development","volume":"4","author":"Ngezahayo","year":"2019","journal-title":"Int. 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Proceedings of the 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway.","DOI":"10.1109\/ICIEA48937.2020.9248317"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3985\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:31:15Z","timestamp":1760128275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,11]]},"references-count":27,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15163985"],"URL":"https:\/\/doi.org\/10.3390\/rs15163985","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,11]]}}}