Scalable Multimodal Approaches

Identifying Vulnerable Populations in

The Republic of the Congo

Artificial intelligence (AI) and machine learning (ML) techniques can generate unbiased geographical data and identify areas of extreme poverty, which policymakers can adopt to prioritize development aid allocations. This innovative method can help develop scalable algorithms to inform humanitarian responses to Africa's food crisis. In the Republic of the Congo (RoC), one out of two people live below poverty, and the pre-COVID level of food insecurity of 10% has surged to 30% during the pandemic. Thus far, governments have responded to this crisis by doubling social protection programs. However, the current targeting methods do not provide enough information on eligible communities and are too slow to mitigate transitory shocks. This issue highlights the need for a systemic, fine-grained, and rapid geographic targeting technique. 

This current project aims to develop a multimodal approach that combines daytime satellite imagery, nighttime luminosity, and social media to estimate the wealth and livelihood of regions and shape aid distribution.

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Recipient of Microsoft
AI for Humanitarian Action Project Initiatives
$25,000 Azure Grant 

The research team established an MOU/ data sharing agreement with the Institut Geographique National (IGN) in the Republic of the Congo. The government agency is providing  geospatial data on Brazzaville and Pointe-Noire, including administrative boundaries, road network, and waterways.

         UPDATE             ....................................................​
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​The team consists of computer/data scientists

and social scientists:​

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• Woojin Jung (PI) - Rutgers, School of Social Work

• Quentin Stoeffler - University of Bordeaux, Economics

• Saeed Ghadimi - University of Waterloo, AI Institute

• Jordan Steiner - Rutgers, School of Social Work

• Maryam Hosseini - New York University, Engineering

• Rofaida Benotsmane - Istanbul Technical Univ., Economics

• Charles Chear - Rutgers, School of Social Work

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• Eunice Park - Rutgers, School of Social Work

• Jacqueline Ponzio - Rutgers, School of Social Work

• Harish Udaya Kumar - Rutgers, Computer Science

• Varun Tej Nookala - Rutgers, Data Sciences

• Devarsh Shah - Rutgers, Computer Science

• Nicy Bazebizonza - Institut Geographique Nationale

The research team represents diversity across disciplines, countries/continents (Africa, Asia, US), languages, and cultures. Our study can contribute to broadening the reach of social protection programs by identifying vulnerable and poor populations in the Republic of the Congo and other countries where there is very limited, georeferenced wealth data.

This current project in the Republic of the Congo was recently featured in 'Focus on Faculty' on Rutgers University's Office of Research website and social media outlets:                 .