Internship Subject M2 - Integrating Earth observation data and deep learning methods to monitor food systems
Roberto Interdonato
Integrating Earth observation data and deep learning methods to monitor food systems
Remote sensing, Machine learning, Earth observation, Food flows
Context :
Food systems are highly interconnected between countries on a global scale, as shown by recent disruptions such as the war in Ukraine and the global pandemic. Food flows are vulnerable to shocks, and these disruptions influence food prices, which in turn affect food consumption patterns. This has had a significant impact on people's diets, particularly in underdeveloped countries where food security is already fragile. However, scientists and policy-makers lack the data and tools to identify weak points in food flows and build food systems resilient to shocks and disruptions. While considerable progress has been made using Earth Observation data to map crop locations and agricultural productivity (e.g. crop yields), little attention has been paid to the intermediate stages of the workflow - distribution, processing and markets - which are key to understanding and modeling how food moves from production to consumption. Additionally, numerous geospatial datasets, such as OpenStreetMap, are publicly accessible and provide valuable information on land use and land cover.
Thanks to advances in artificial intelligence and its application to Earth Observation data, continuously collected satellite images on a global scale, combined with meteorological data, make it possible to monitor food systems in real time. Deep learning models, capable of capturing complex, non-linear relationships, and multimodal algorithms integrating data from a variety of sources, are opening up new perspectives in this field. This internship proposes to exploit multi-temporal and multi-resolution Earth observation data, by combining them with learning models, to monitor food systems, estimate agricultural yields and analyze their links with market prices.
This internship focuses on developing machine learning approaches to analyze food flows in Rwanda, in relation to food security situation in the country, by using comprehensive market data and geospatial information. Food flows often deviate from optimal distribution patterns due to infrastructure constraints, market dynamics, and socio-economic factors. For example, a certain product (e.g., potatoes) grown in northern regions may follow suboptimal routes to reach southern markets. By modeling both ideal and actual food flows, we can identify bottlenecks and opportunities to improve food security.
Missions :
The project aims to understand the relationship between food production locations, distribution networks, and market accessibility to inform food security policies. More specifically, the final task is to build a machine learning model able to predict the probability that a certain item is sold in a specific market, based on production and distribution data.
The project leverages two primary datasets:
· Public Market Dataset: 1.2 million items across 70 markets covering 10 types of food items.
· CGIAR/IITA Survey Database: A dataset collected by the IITA (International Institute of Tropical Agriculture) including monthly data from 7,000 vendors across 67 markets in all districts of Rwanda, including food quality assessments and detailed market information.
These datasets will be complemented by geospatial data including OpenStreetMap (OSM) infrastructure data, land cover information, and Earth observation data (NDVI and other spectral indices).
The main tasks to address during the internship will be:
1. Database Integration and Market Mapping
a. Merge the public market dataset with CGIAR/IITA survey data to create a comprehensive market database
b. Map which specific food items are sold in which markets
2. Geospatial Data Integration
a. Incorporate OpenStreetMap data to understand transportation networks and market accessibility
b. Integrate land cover and agricultural production data to identify food production zones
c. Process Earth observation data (NDVI, meteorological data) to assess agricultural productivity
d. Map the complete food system from production areas to consumption markets
3. Machine Learning Model Development
a. Develop predictive models to estimate the probability that specific food items will be available in particular markets
b. Compare actual food flows with modeled optimal flows to identify inefficiencies
c. Test developed models against baseline methodologies and state-of-the-art approaches
4. Writing of the internship report (in English) to capitalize on the work carried out with a view to a possible scientific publication. If possible, also release associate code and data.
Skills required :
- Programming skills
- Interest in data analysis
- Scientific rigor
- Curiosity and open-mindedness
- Analytical, writing and summarizing skills
How to apply :
Send CV, cover letter and M1 (or 4th year) transcript to :
simon...@cirad.fr , roberto.i...@cirad.fr
specifying as
e-mail subject “CANDIDATURE STAGE DIGITAG”.
Additional Information :
- Duration of 6 months, starting February 2025
- Remuneration: CIRAD salary scale, ~600 euros/month
- The internship will take place at CIRAD, in the UMR TETIS (Territory, Environment,
Remote Sensing and Spatial Information), located at the Maison de la
Télédétection in Montpellier.
- The internship will be carried out in collaboration with Assistant Professor
Claudia Paris and Yue Dou, currently working at the ITC Faculty of Geographic Information
Science and Earth Observation, University of Twente, Netherlands.