Announcements

Title : From landscape detection to population dynamics

Abstract :

This PhD project aims at improving knowledge on population dynamics by 
recognition of landscape indicators retrieved  from high-resolution 
satellite images. In the regions where demographic data is scarce or 
unsatisfying, remotely sensed data can be an efficient alternative to 
provide a continuous flow of valuable information. The research will 
therefore focus on the production of indicators qualifying the 
anthropogenic footprint on given milieux, accounting for its intensity, 
and will then analyse the possible links between these indicators and 
population dynamics.

Madagascar provides an excellent setting for studying the interaction 
between rural landscape and population, with the recent creation of a 
Laboratoire Mixte International « Paysage », involving various research 
teams anchored on local field sites. This project is based on Copernicus 
satellite data (Spot and Sentinel), national population censuses (1993 
and 2018), surveys and a local comprehensive demographic field survey 
that is part of an ongoing IRD project planned at LPED (Forhum-DD : 
Interactions Rural Forests-Human populations and sustainable 
development, resubmitted to the French ANR in 2021) on the Malagasy 
highlands, to the west of the town of Arivonimamo. The landscape 
indicators, defined at various levels centred on this field site, will 
then be tested in a different area, the common field site of the LMI 
Paysage, located in the northern forest corridor of Ranomafana-Andringitra.

More information : 

https://recrutement.cnes.fr/fr/annonce/1498713-151-from-landscape-detection-to-population-dynamics-93300-aubervilliers

Title : Prediction of demographic indicators from remote sensing images

Abstract : 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.

For more information : PhD call Lipade-Ined

L’UR15 « Démographie des populations du Sud » recherche un·e chargé·e d’études pour effectuer un état des lieux des connaissances sur migration, environnement et politiques publiques en Afrique au Sud du Sahara dans le cadre d’un projet en partenariat avec l’Agence Française de Développement.

Pour en savoir plus : Chargé-e études Migration-environnement-politiques publiques

Modern remote sensing makes it possible to monitor over time environmental changes in rural and urban areas: land use, deforestation, urbanisation, plant cover, roads, water points, etc. These changes in the environment interact in various ways with demographic, health and socio-economic developments. Territorial disparities in access to resources or infrastructure, in activities (farming or industrial) and living conditions may play a key role in individual characteristics and trajectories. In the Global South, the environment is changing rapidly under the combined effect of population growth, urbanisation and development. Increasingly frequent and violent climatic events accentuate these trends. The impact of these changes on migration, families and health has not been extensively examined.

You will undertake research into understanding the interactions between the environment and population dynamics in the Global South on the basis of demographic and environmental data. The demographic data will come from existing sources, produced locally and nationally. The environmental data will be based on European satellite imagery. The research will be aligned with the themes of the Population, Climate and Environment axis of INED’s research unit 15, dedicated to the demography of the Global South (Demosud). You will also be called upon, working with other project members and the statistical methods department, to organise training and dissemination sessions on the use of remote sensing in demography.

Please send a detailed CV, covering letter, two examples of research texts (and their publication status), contact details of two referees who may be contacted after shortlisting, to ipops@ined.fr by March 15, 2021 at the latest.

For more information : https://www.ipops.fr/en/recruitment_training/call-for-applications-recruitment-of-a-researcher-environment-demographic-dynamics-and-inequalities-in-developing-countries/

Titre: Automatic production of environmental indicators from freely available remote sensing data: from a global to a local scale

Résumé: This collaborative project aims at studying the feasibility of automatically producing repeatable indicators from remote sensing data in Africa to allow for spatially complete and temporally up-to-date information. To this effect, we will use freely available Sentinel 2 images (produced by the European Space Agency) to produce standardised environmental indicators, in the form of local climate zones. The student will study the relevance of a convolutional neural network-based method for this task. She will also explore the possibility of embedding spatio-temporal relations in such a model, and quantify the benefits. Finally, a reflection on the relevance of these results for demographic studies will be conducted, as well as a graphical user interface allowing to produce such indicators given a remote sensing image.