Jean Eudes is one of the #DigitAg co-funded PhDs
Leverage Multi-Source Remote Sensing data via machine learning to improve Crop Monitoring Systems
- Start Date: November 2018
- University: University of Montpellier
- PhD School: I2S Information Structures Systèmes
- Field(s): Computer Science
- Doctoral Thesis Advisor: Dino Ienco, Irstea Tetis
- Co-supervisors : Dino Ienco, Irstea Tetis & Louise Leroux, Cirad Aida
- Funding: #DigitAg – Irstea
- #DigitAg: Axis 5 – Challenges 8 & 6
Keywords: Machine learning, deep learning, Multi-source and Multi-temporal Remote sensing, Land Cover, Crop Yield Estimation
Face to population increasing and the environmental impacts of climate change, ensuring the food security of populations while promoting sustainable agriculture that preserves terrestrial ecosystems and biodiversity (Goals 2 and 15 of the UN Sustainable Development) becomes a major challenge for the future of our society. As satellite missions multiply (eg Sentinel), various sources of information are now available to better characterize agricultural systems and associated practices at the regional, national and global scales. At the same time, the integration of these various sources of data, especially for agronomic issues, remains a real challenge.
The aim of this Ph.D. is to propose new machine learning approaches particularly with deep learning to integrate the multi-source and the multi-temporal information provided by optical and radar time series and very high spatial resolution imagery in order to improve the characterization of cultivated areas and the estimation of agricultural yields from data collected in the field.
The developed approaches will be evaluated with a cross look at contrasting sites in terms of crop systems and / or agricultural practices (France, Senegal).
Contact : jean-eudes.gbodjo [AT] irstea.fr – Tél : 04 67 54 87 54