SebastianHafner/DDA_PopulationMapping

[JAG] Deep learning-based population mapping from Earth observation data in Sub-Saharan Africa Code for the following paper: S. Georganos, S. Hafner, M. Kuffer, C. Linard and Y. Ban, "A census from heaven: Unravelling the potential of deep learning and Earth observation for intra-urban population mapping in data scarce environments," in *International Journal of Applied Earth Observation and Geoinformation*, 114, 103013. [Paper] # Abstract Urban population distribution maps are vital elements for monitoring the Sustainable Development Goals, appropriately allocating resources such as vaccination campaigns, and facilitating evidence-based decision making. Typically, population distribution maps are derived from census data from the region of interest. Nevertheless, in several low- and middle-income countries, census information may be unreliable, outdated or unsuitable for spatial analysis at the intra-urban level, which poses severe limitations in the development of urban population maps of adequate quality. To address these shortcomings, we deploy a novel framework utilizing multisource Earth Observation (EO) information such as Sentinel-2 and very-high-resolution Pleiades imagery, openly available building footprint datasets, and deep learning (DL) architectures, providing end-to-end solutions to the production of high quality intra-urban population distribution maps in data scarce contexts. Using several case studies in a Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania), our results emphasize that the combination of DL and EO data is very potent and can successfully capture relationships between the retrieved image features and population counts at fine spatial resolutions (100 meter). Moreover, for the first time, we used state-of-the-art domain adaptation methods to predict population distributions in Dar es Salaam and Nairobi (R$^2$ = 0.39 – 0.60) that did not require national census or survey data from Kenya or Tanzania, but only a sample of training locations from Dakar. The DL architecture is based on a m …