Abstract
Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.
Highlights
The human population is one of the core elements influencing sustainable development
We investigate if the World Settlement Footprint—3D (WSF3D) dataset can be used to effectively identify and eliminate large industrial/commercial areas from the built-up environment, which in the past have been reported as major sources of under/overestimation errors in population modelling
At the country scale, as seen from the distribution of the per-class percentage share presented in Figure 8, the proportion of built-up settlements pixels predicted as industrial and non-industrial types by the FM-Random Forest (RF) models were fairly comparable to those reported by the reference maps
Summary
The human population is one of the core elements influencing sustainable development. (positively and negatively) the social, economic and environmental development of any given region [1,2]. On this basis, to effectively implement and monitor sustainable policies, governments, researchers and policymakers around the world need to have access to high-quality, timely, reliable and spatially explicit population data. To respond to the increasing demand for robust population data, new global and continental geospatial datasets that describe the size, extent and spatial distribution of the human population are constantly being produced, harnessing the accelerated development of Earth Observation (EO) technologies [4,5,6,7]. State-of-the-art gridded population datasets produced through the synergies of Remote Sensing (RS) and Geographical Information Systems (GIS), such as the Gridded Population of the World (GPW) [8], the Global
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