Offre en lien avec l’Action/le Réseau : MACLEAN/– — –

Laboratoire/Entreprise : Université de Paris / INED
Durée : 3 ans
Contact : camille.kurtz@parisdescartes.fr
Date limite de publication : 2021-07-08

Contexte :
General information
• When to apply: until the 12th of May 2021
• Starting date: October 2021
• Funding: EDITE doctoral school (subject to being approved by the EDITE committee.)
• Institutes: Université de Paris, Laboratoire d’Informatique Paris Descartes (LIPADE), équipe Systèmes Intelligents de Perception and Institut national d’études démographiques, unité Démographie des populations du Sud (Demosud)
• Location: 45 rue des Saints-Pères, 75006 Paris (LIPADE) and 9, cours des Humanités, 93322 Aubervilliers (INED)
• Supervision: Sylvain Lobry, Camille Kurtz, Laurent Wendling – (first.lastname@u-paris.fr), Géraldine Duthé, Valérie Golaz (first.lastname@ined.fr)
• Keywords: Remote sensing, Demography, Computer vision, Deep learning, Africa

Sujet :
Objective
In this PhD, which stems from and strenghtens an on-going collaboration between LIPADE and INED, the candidate will develop deep learning based methodologies using remote sensing data to
predict indicators of the environment and environmental change, for demographic analysis. As such, the objective of this topic is twofold: to propose methodological contributions for the large-scale extraction of diachronic environmental indicators and to analyze their contribution to spatial population and health analyses. How do these indicators compare with the existing environmental data?
What results do they yield in terms of the impact of environmental characteristics and environmental change on population structure and health in Sub-Saharan Africa? We expect prime results in the field of computer science (innovative methodologies) and demography (a better understanding of local inequalities in terms of population structure and health) as well as a contribution to the use of fine remote sensing data analysis for population studies.

Context
In a globalized context increasingly impacted by climate change, undergoing rapid population growth and urbanization, demographic studies would gain to better take 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 environmental data are seldom available. One major harmonized source of data on population and health in global South countries is the Demographic and Health Surveys (DHS) program. Since 2015, Demographic and Health Surveys were conducted once in about all the countries that participate in the program across Sub-Saharan Africa. To date, in spite of the delays in data collection and data set preparation due to the Covid-19 lockdowns, 16 data sets are already available for use, with matching geodata files. The large amount of spatial data regularly acquired since 2015 (in 2019 only, Sentinel satellites from the European Space Agency produced 7.54 PiB of open-access data 1 ) are an opportunity to produce standardized and up-to-date indicators.

Profil du candidat :
We are looking for a student in Master 2 or engineering school in computer science data science or demography.

Formation et compétences requises :
The ideal candidate would have knowledge in image processing, computer vision, machine learning, Python programming, statistical data analysis and demographic research. The candidate should have an interest in large scale studies, remote sensing and demography.

Adresse d’emploi :
45 rue des Saints-Pères, 75006 Paris (LIPADE) and 9, cours des Humanités, 93322 Aubervilliers (INED)

Document attaché : 202105030918_PhD – Prediction of demographic indicators from remote sensing images.pdf