Conférence : Advancing Global Food Security and SDGs with Machine Learning and Earth Observations

Conférence : "Advancing Global Food Security and SDGs with Machine Learning and Earth Observations"

Dans le cadre du séminaire "le deep learning appliqué à l'agriculture", Hannah Kerner (Assistante de recherche au sein de l'équipe HARVEST de l'équipe Maryland) interviendra lors d'une conférence intitulée "Advancing Global Food Security and SDGs with Machine Learning and Earth Observations" qui aura lieu ce Jeudi 18 novembre 2021 à 14h, en salle A301 sur le site d'Illkirch. 

Résumé de la conférence

Title : "Advancing Global Food Security and SDGs with Machine Learning and Earth Observations"

Abstract : satellite Earth observation (EO) data is rapidly gaining interest in the AI community due to the massive datasets involved as well as the opportunities for using AI and EO data to address urgent challenges related to climate change, the environment, agriculture and food security, and humanitarian needs. However there are currently many challenges for developing AI systems that use EO data for practical applications, namely, there are limited public labeled datasets, a lack of harmonization across labels and source data, and existing algorithms do not account for geographic context. In this talk, I'll present some of the approaches we are developing to address these challenges to create AI+EO systems that can be integrated into real-world applications for advancing global food security and other sustainable sevelopment goals.

lien pour suivre la conférence à distance : https://umd.zoom.us/my/hkerner