{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:03:05Z","timestamp":1774317785617,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002749","name":"Belgian Federal Science Policy Office","doi-asserted-by":"publisher","award":["SR\/00\/304"],"award-info":[{"award-number":["SR\/00\/304"]}],"id":[{"id":"10.13039\/501100002749","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>By 2050, half of the net increase in the world\u2019s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the region. In this context, Earth Observation (EO) is an opportunity to gather accurate and up-to-date spatial information on urban extents. During the last decade, the adoption of open-access policies by major EO programs (CBERS, Landsat, Sentinel) has allowed the production of several global high resolution (10\u201330 m) maps of human settlements. However, mapping accuracies in SSA are usually lower, limited by the lack of reference datasets to support the training and the validation of the classification models. Here we propose a mapping approach based on multi-sensor satellite imagery (Landsat, Sentinel-1, Envisat, ERS) and volunteered geographic information (OpenStreetMap) to solve the challenges of urban remote sensing in SSA. The proposed mapping approach is assessed in 17 case studies for an average F1-score of 0.93, and applied in 45 urban areas of SSA to produce a dataset of urban expansion from 1995 to 2015. Across the case studies, built-up areas averaged a compound annual growth rate of 5.5% between 1995 and 2015. The comparison with local population dynamics reveals the heterogeneity of urban dynamics in SSA. Overall, population densities in built-up areas are decreasing. However, the impact of population growth on urban expansion differs depending on the size of the urban area and its income class.<\/jats:p>","DOI":"10.3390\/rs13030525","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T13:01:12Z","timestamp":1612270872000},"page":"525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Mapping 20 Years of Urban Expansion in 45 Urban Areas of Sub-Saharan Africa"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7275-7967","authenticated-orcid":false,"given":"Yann","family":"Forget","sequence":"first","affiliation":[{"name":"Spatial Epidemiology Lab, Universit\u00e9 Libre de Bruxelles, B-1050 Brussels, Belgium"},{"name":"Signal Image Centre, Royal Military Academy, B-1000 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5487-6137","authenticated-orcid":false,"given":"Michal","family":"Shimoni","sequence":"additional","affiliation":[{"name":"Signal Image Centre, Royal Military Academy, B-1000 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3708-3359","authenticated-orcid":false,"given":"Marius","family":"Gilbert","sequence":"additional","affiliation":[{"name":"Spatial Epidemiology Lab, Universit\u00e9 Libre de Bruxelles, B-1050 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0819-7755","authenticated-orcid":false,"given":"Catherine","family":"Linard","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Namur, B-5000 Brussels, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2019). 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