Remote Sensing for Demographic studies (2020 - Today)
In a globalized context increasingly impacted by climate change, demographic studies would gain from taking environmental data into account and be carried out at the transnational level. However, this is not always possible in sub-Saharan Africa, as matching harmonized demographic and environ- mental data are seldom available. The large amount of data regularly acquired since 2015 (in 2019 only, Sentinel satellites from the European Space Agency produced 7.54 PiB of open-access data) provides an opportunity to produce relevant standardized indicators at the global scale. In this project, we aim at producing such indicators that can be matched to demographic surveys.
This project is the result of a partnership with INED and is funded by the DiiP.
Valérie Golaz (INED)
Géraldine Duthé (INED)
Basile Rousse (2021-Today)
Lys Thay (2021)
Seasonal semi-supervised domain adaptation for linking population studies and Local Climate Zones (to appear)
Environment and demographic dynamics are strongly linked. However, relevant data to study this interaction may be scarce especially in sub-Saharan Africa where it is not always possible to perform such studies with a high temporal frequency. Satellite imagery, when linked to demographic data, can be a significant asset to estimate missing data as it covers every country with both high spatial and temporal resolution. We aim to take advantage of satellite data to characterize the environment in inter-tropical areas. This environment is regulated by the changing of two seasons that are essential to consider. We introduce a semi-supervised domain adaptation strategy for neural networks based on seasonal changes. This strategy can be used to produce land cover maps in regions of the world where limited labeled datasets are available. We apply this method to produce environmental indicators and link them to malaria rates from the Malaria Indicator Survey of Burkina Faso. We show that malaria rates are correlated not only to urbanisation but also to the environmental characterisation of studied areas.
Matching environmental data produced from remote sensing images to demographic data in Sub-Saharan Africa