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Developments in Remote Sensing and Population Modelling

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 390

Special Issue Editors


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Guest Editor
Department of Geography, Université de Namur, Namur, Belgium
Interests: population distribution modelling; health geography; geospatial analysis; spatial inequalities
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Geosciences, Environment & Society, Université Libre de Bruxelles (ULB), Bruxelles, Belgium
Interests: geospatial analysis; geography; machine learning; remote sensing; geographic object-based image analysis; deep learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department Geosciences, Environment and Society, Université Libre de Bruxelles, Bruxelles, Belgium
Interests: remote sensing; spatial analysis; machine learning; spatial epidemiology; geostatistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Accurate and systematic population estimates across the globe, primarily in the Global South, are crucial pieces of information in order to meet the Sustainable Development Goals set by the United Nations, reducing inequalities and promoting pro-poor policies. Harnessing the power of remote sensing, Geographic Information Systems, geostatistical, and machine learning techniques, it is possible to provide reliable population predictions at various scales (i.e., urban, regional, national, continental).

This Special Issue welcomes recent developments related to:

  • Improving the modeling techniques coupling Earth Observation and population data;
  • Innovative ways to combine remote sensing with other types of ancillary features such as OpenStreetMap data and mobile phone information for population estimation;
  • Proposing new methods to distribute population in both bottom-up and top-down approaches using remote sensing data;
  • Exploring the effects of spatial scale in population distribution models primarily relying on Earth Observation information;
  • Applications of existing methods in regions where population information is scarce.

Grippa, T., Linard, C., Lennert, M., Georganos, S., Mboga, N., Vanhuysse, S., ... & Wolff, E. (2019). Improving urban population distribution models with very-high-resolution satellite information. Data, 4(1), 13.

Georganos, S., Grippa, T., Niang Gadiaga, A., Linard, C., Lennert, M., Vanhuysse, S., ... & Kalogirou, S. (2019). Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modeling. Geocarto International, 1–16.

Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., ... & Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45), 15888–15893.

Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2).

Gaughan, A. E., Stevens, F. R., Linard, C., Jia, P., & Tatem, A. J. (2013). High-resolution population distribution maps for Southeast Asia in 2010 and 2015. PloS one, 8(2).

Linard, C., Gilbert, M., Snow, R.W., Noor, A.M., Tatem, A.J., (2012). Population Distribution, Settlement Patterns and Accessibility across Africa in 2010. Plos One, 7(2): e31743.

Dr. Catherine Linard
Dr. Tais Grippa
Mr. Stefanos Georganos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • population
  • machine learning
  • dasymetric distribution
  • remote sensing
  • geographic information systems

Published Papers

There is no accepted submissions to this special issue at this moment.
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