{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:47:52Z","timestamp":1779202072439,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T00:00:00Z","timestamp":1594771200000},"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>The availability of free and open data from Earth observation programmes such as Copernicus, and from collaborative projects such as Open Street Map (OSM), enables low cost artificial intelligence (AI) based monitoring applications. This creates opportunities, particularly in developing countries with scarce economic resources, for large\u2013scale monitoring in remote regions. A significant portion of Earth\u2019s surface comprises desert dune fields, where shifting sand affects infrastructure and hinders movement. A robust, cost\u2013effective and scalable methodology is proposed for road detection and monitoring in regions covered by desert sand. The technique uses Copernicus Sentinel\u20131 synthetic aperture radar (SAR) satellite data as an input to a deep learning model based on the U\u2013Net architecture for image segmentation. OSM data is used for model training. The method comprises two steps: The first involves processing time series of Sentinel\u20131 SAR interferometric wide swath (IW) acquisitions in the same geometry to produce multitemporal backscatter and coherence averages. These are divided into patches and matched with masks of OSM roads to form the training data, the quantity of which is increased through data augmentation. The second step includes the U\u2013Net deep learning workflow. The methodology has been applied to three different dune fields in Africa and Asia. A performance evaluation through the calculation of the Jaccard similarity coefficient was carried out for each area, and ranges from 84% to 89% for the best available input. The rank distance, calculated from the completeness and correctness percentages, was also calculated and ranged from 75% to 80%. Over all areas there are more missed detections than false positives. In some cases, this was due to mixed infrastructure in the same resolution cell of the input SAR data. Drift sand and dune migration covering infrastructure is a concern in many desert regions, and broken segments in the resulting road detections are sometimes due to sand burial. The results also show that, in most cases, the Sentinel\u20131 vertical transmit\u2013vertical receive (VV) backscatter averages alone constitute the best input to the U\u2013Net model. The detection and monitoring of roads in desert areas are key concerns, particularly given a growing population increasingly on the move.<\/jats:p>","DOI":"10.3390\/rs12142274","type":"journal-article","created":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T10:54:46Z","timestamp":1594896886000},"page":"2274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Deep Learning with Open Data for Desert Road Mapping"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-8271","authenticated-orcid":false,"given":"Christopher","family":"Stewart","sequence":"first","affiliation":[{"name":"European Space Agency (ESA), Earth Observation Programmes, Future Systems Department, 00044 Frascati, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michele","family":"Lazzarini","sequence":"additional","affiliation":[{"name":"European Union Satellite Centre (SatCen), 28850 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrian","family":"Luna","sequence":"additional","affiliation":[{"name":"European Union Satellite Centre (SatCen), 28850 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergio","family":"Albani","sequence":"additional","affiliation":[{"name":"European Union Satellite Centre (SatCen), 28850 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1109\/LGRS.2018.2864342","article-title":"Road segmentation in SAR satellite images with deep fully convolutional neural networks","volume":"15","author":"Henry","year":"2018","journal-title":"IEEE Geosci. 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