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 472
Special Issue Editors
Interests: population distribution modelling; health geography; geospatial analysis; spatial inequalities
Special Issues, Collections and Topics in MDPI journals
Interests: geospatial analysis; geography; machine learning; remote sensing; geographic object-based image analysis; deep learning
Special Issues, Collections and Topics in MDPI journals
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 LinardDr. Tais Grippa
Mr. Stefanos Georganos
Guest Editors
Manuscript Submission Information
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Keywords
- population
- machine learning
- dasymetric distribution
- remote sensing
- geographic information systems
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