Domain Adaptation for Mapping LCZs in Sub-Saharan Africa With Remote Sensing: A Comprehensive Approach to Health Data Analysis

Abstract

Environment and population are closely linked, but their interactions remain challenging to assess. To fill this gap, modeling the environment at a fine resolution brings a significant value, if combined with population-based studies. This is particularly challenging in regions where the availability of both population and environmental data are limited. In low- and middle-income countries, many demographic and health data are from nationally representative household surveys, which now provide approximate geolocations of the sampled households. In parallel, freely available remote sensing data, due to their high spatial and temporal resolution, make it possible to capture the local environment at any time. This study aims to correlate standard demographic and health information with a high-resolution environment characterization derived from satellite data, encompassing both rural and urban areas in Sub-Saharan Africa. We use the malaria indicator survey conducted in 2017–2018 in Burkina Faso. We first present a deep semisupervised domain adaptation strategy based on the intertropical climatic characteristics of the country for precisely mapping local climate zones (LCZs). This strategy models seasonal variations through contrastive learning to extract useful information for the mapping process. We then use this high-resolution LCZ map to characterize, in four groups, the immediate environment of the sampled households. We find a significant association between these local environments and malaria among households' children. Going beyond the traditional dichotomous urban/rural characterization, our results provide interesting insights for public health. This innovative method offers new avenues for exploring population and environment interactions, especially in the growing climate change concern.

Keywords

• Deep learning
• demography
• domain adaptation (DA)
• land cover
• remote sensing