Abstract
Recent research has shown promising results for estimating structural area, volume, and population from Sentinel 1 and 2 data at a 10 by 10-m spatial resolution. These studies were, however, conducted in homogeneous countries in Northern Europe. This study presents a deep learning methodology for population estimation in areas geographically distinct from Northern Europe. The two case study areas are Ghana and Egypt's Mediterranean coast, with supplementary ground truth data collected from Uganda, Kenya, Tanzania, Palestine, and Israel. This study aims to answer the question: How can we use Deep Learning to map structural area and type to derive population estimates for Ghana and Egypt based on Sentinel data? At 10 by 10-m resolution, the accuracy of the presented area predictions is similar to the Google Open Buildings dataset. An intercomparison of the presented population predictions is made with global state-of-the-art spatial population estimates, and the results are promising, with the proposed methodology showing comparable or better results than the state-of-the-art for the study areas.
Original language | English |
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Journal | Transactions in GIS |
Volume | 26 |
Issue number | 8 |
Pages (from-to) | 3147-3175 |
Number of pages | 29 |
ISSN | 1361-1682 |
DOIs | |
Publication status | Published - Dec 2022 |
Bibliographical note
Funding Information:Casper Fibæk has received funding from the Danish Innovation Fund and the NIRAS/Alectia Foundation. We would like to thank Professor Hany Ayad from the University of Alexandria and Foster Mensah from the University of Ghana for assistance in collecting and verifying ground truth data. Open access funding enabled and organized by ProjektDEAL.
Publisher Copyright:
© 2022 The Authors. Transactions in GIS published by John Wiley & Sons Ltd.