Preprint Article Version 1 This version is not peer-reviewed

Modelling Built-Settlements between Remotely-Sensed Observations

Version 1 : Received: 18 December 2018 / Approved: 20 December 2018 / Online: 20 December 2018 (12:55:37 CET)
Version 2 : Received: 8 April 2019 / Approved: 8 April 2019 / Online: 8 April 2019 (13:24:22 CEST)

How to cite: Nieves, J.J.; Sorichetta, A.; Linard, C..; Bondarenko, M.; Steele, J.; Stevens, F.; Gaughan, A.E.; Carioli, A.; Clarke, D.; Esch, T.; Tatem, A.J. Modelling Built-Settlements between Remotely-Sensed Observations. Preprints 2018, 2018120250 (doi: 10.20944/preprints201812.0250.v1). Nieves, J.J.; Sorichetta, A.; Linard, C..; Bondarenko, M.; Steele, J.; Stevens, F.; Gaughan, A.E.; Carioli, A.; Clarke, D.; Esch, T.; Tatem, A.J. Modelling Built-Settlements between Remotely-Sensed Observations. Preprints 2018, 2018120250 (doi: 10.20944/preprints201812.0250.v1).

Abstract

Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category “built” in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.

Supplementary and Associated Material

https://data.mendeley.com/datasets/f366zsg6hh/1: Data, Code, and supplementary materials

Subject Areas

Built, urban growth, random forest, dasymetric, population

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