Abstract
The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.
Highlights
In the context of global sustainable development, the adoption of the United Nations (UN) Sustainable Development Goals (SDGs) and post-2015 international development agreements ignited a much-needed data revolution, in which countries and institutions all around the world started recognising the fundamental role of geospatial data for policy making [1,2]
The end-user WSF2019-Pop dataset for the African continent depicts the residential population for the year 2019 adjusted to the UN national total estimates
The final dataset has a spatial resolution of 0.3 arc-sec (~10 m at the Equator), a WGS84 Geographic Coordinate System projection, and represents the number of people per pixel
Summary
In the context of global sustainable development, the adoption of the United Nations (UN) Sustainable Development Goals (SDGs) and post-2015 international development agreements ignited a much-needed data revolution, in which countries and institutions all around the world started recognising the fundamental role of geospatial data for policy making [1,2]. High-quality geospatial datasets, in particular those derived from Earth Observation (EO) technologies, are becoming an essential source of information, Remote Sens. 2021, 13, 1142 needed for guiding social, economic and environmental policies at global, regional, national and subnational scales [3,4]. Compared to ground-based methods, the use of EO technologies, and in particular the use of satellites, allows the production of cost-effective data with a higher frequency over longer periods of time and over larger spatial extents [5,6]. When combined with traditional data (e.g., field surveys, census data, demographic and socio-economic statistics), EO data (satellite imagery) supplement and/or enhance the quality of the information by improving its spatial resolution and interpretation capabilities (including better visualization) [3]
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