Vasileios Sitokonstantinou
Machine learning for Earth and Environmental science
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Postdoc researcher @ Image Processing Lab, Universitat de València
eMail: firstname.lastname@uv.es
I'm a postdoc researcher with Gustau Camps-Valls at the ISP group at the University of Valencia. I develop machine learning methods for Earth observation (ML4EO) grounded in causality and interpretability. I use remote sensing observations, environmental data, and land use information to analyze the geospatially heterogeneous impact of human actions and weather/climate events on (agro)ecosystem services. I aim to offer data-driven insights for tailored sustainable policies that enhance climate change resilience and expand the global carbon sink while securing food for all. See Research for more information.
Before my current role, I was leading AgriHUB at the National Observatory of Athens, focusing on applied research in ML4EO for developing applications for climate-smart farming in collaboration with industrial partners and for monitoring the Common Agricultural Policy (CAP) of the EU. I obtained my PhD from the National Technical University of Athens, where my research on Big Earth data and machine learning for sustainable agriculture laid the foundation for much of my current work. I also hold MSc degrees from the National Observatory of Athens in Remote Sensing and University College London in Wireless Communications, and a BEng in Electrical and Electronic Engineering from Imperial College London.
Keywords: machine learning, remote sensing, causality, Earth system science, sustainable agriculture, food security
News
News
04/2025 -New paper: Leveraging Causality and Explainability in Digital Agriculture in Environmental Data Science
01/2025 -New paper: Large Language Models for Causal Hypothesis Generation in Science in Machine Learning: Science and Technology
12/2024 - New paper: Cloud gap-filling with deep learning for improved grassland monitoring in Computers and Electronics in Agriculture
08/2024 - Preprint of my perspectives on Causal machine learning for sustainable agroecosystems
07/2024 - Attended IEEE IGARSS2024. The two papers presented are: 1. Transfer learning for global crop maps, 2. Causal ML for assessing humanitarian aid on food security. I co-organized and co-chaired the session on Causality and Machine Learning for Sustainable Agriculture and Food Security.
06/2024 - New paper: Causal discovery reveals complex patterns of drought-induced displacement in iScience
05/2024 - Attended ESA's EO4Agri2024. The three works presented are: 1. ML for national yield forecasting, 2. Personalizing practices w/ causal ML, 3. Causal ML for food security
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