I am a PhD student specializing in Earth Observation at the University of Malaga. I hold a BSc in Sound and Image Engineering and an MSc in Acoustic Engineering, both from the same institution.
My research journey began with a collaboration on a project framed within the NASA Mars 2020 mission during my Bachelor’s thesis. Following my Master’s, I had the opportunity to contribute to an ESA-funded SatNEx project regarding the integration of semantic and goal-oriented communications in satellite systems, which launched my doctoral studies. Currently, my research focuses on satellite mission planning and scheduling, where I design algorithms to solve complex combinatorial optimization problems.
In 2025, I was awarded an IMFAHE Excellence Fellowship to carry out a three-month research stay at a leading international institution, which I will complete at ESA Φ-lab. Here, I will explore the use of artificial intelligence on data acquisition scheduling in order to enhance the autonomy, efficiency, and resilience of satellite missions.
I’m a PhD researcher in Sustainable Development and Climate Change at the University School for Advanced Studies IUSS Pavia, focusing on coastal vulnerability assessment. I hold a Bachelor’s degree in Natural Sciences from Sapienza University of Rome and a Master’s degree in Marine Ecobiology from the same university. I completed a research fellowship at the Italian Institute for Environmental Protection and Research (ISPRA), where I worked on the vulnerability of marine and coastal ecosystems to climate change
Simon Donike is a PhD researcher in Artificial Intelligence and Earth Observation at the Image and Signal Processing Group, Universitat de València. His research focuses on generative AI for Earth-observation data, spanning a range of model families and applications for satellite imagery analysis and synthesis. Working within the ESA OpenSR initiative, he develops and validates trustworthy generative methods that support enhanced data quality, reconstruction, and analysis for downstream Earth-system applications.
Lennart van der Peet received his BSc in Aerospace Engineering from Delft University of Technology, where he currently pursues an Honours MSc in Space Engineering.
Between his degrees, Lennart served as chief engineer for AeroDelft, a student-led initiative to prove and promote liquid hydrogen as an alternative to conventional fuels in aviation. He continues to support the organisation as advisor to the board through July 2026.
Currently, Lennart is a Visiting Researcher at the ESA Φ-lab (January–April 2026). His research there focuses on reducing the synthetic aperture radar duty cycle using generative artificial intelligence, as part of his MSc thesis.
I am Kenzo Bounegta, a 26-year-old researcher specializing in Deep Learning and Computer Vision for satellite imagery. I hold a Master’s degree in AI from Ecole Polytechnique and HEC Paris. I have worked on flood detection, land use classification, and detection of looted archeological sites in Afghanistan using optical and SAR satellite data. I am passionate about multimodal Geo-FM.
I am Juan Francisco Amieva, a researcher in Earth Observation and Artificial Intelligence, currently pursuing an industrial PhD in AI between Tracasa Instrumental and the Public University of Navarre (UPNA). My work focuses on deep learning for Synthetic Aperture Radar (SAR) image super-resolution, and as a Visiting Researcher at ESA Φ-lab, I contribute to the development of a foundational model for multi-frequency SAR to improve large-scale forest characterization.
I hold an M.Sc. in Geoinformatics Engineering (with honors) from Politecnico di Milano; a Master in Data Intelligence (Big Data orientation) from the National University of La Plata; a Specialization in Transport Policy and Planning from the National University of San Martín; and a degree in Industrial Engineering from the National University of La Plata.
Over the past three years, I have contributed to Tracasa Instrumental S.L., developing AI-driven solutions that combine satellite data analysis and geospatial intelligence for research and operational applications.
Currently, I serve as a Data Scientist in Earth Observation at Tracasa Instrumental (Spain), where I develop deep learning models for remote sensing applications, with a particular focus on SAR image enhancement techniques.
My expertise lies in the integration of Earth Observation and AI for satellite data analysis. I work with SAR, multispectral, and hyperspectral imagery, developing deep learning models for a wide range of applications, including chlorophyll-a estimation in lakes, crop yield prediction, density estimation, object detection, super-resolution, and change detection. This combination of AI and EO techniques allows me to design scalable and transferable models that extract meaningful information from complex satellite datasets. My work aligns closely with ESA Φ-lab CIN’s mission of fostering AI innovation in Earth Observation and advancing the development of next-generation geospatial models.
I am a data scientist in the field of geospatial artificial intelligence. My academic journey began at the University of Münster, where I earned a B.Sc. in Information Systems. Professionally, I have dedicated my work to sustainable development in national and international non-governmental organisations, such as the United Nations Development Programme or the Bertelsmann Foundation.
I am currently pursuing a master’s degree in Geoinformatics and Spatial Data Science at the Institute for Geoinformatics (IfGI) at the University of Münster, with a focus on computer vision and machine learning for sustainable data-driven systems. My expertise lies at the intersection of data science and machine learning, geospatial data, and their application in sustainable development. These specialisations allow me to integrate methodological foundations with practical solutions, designing and analysing systems that enable decision-making from complex Earth observation data. This approach directly supports ESA Φ-lab CIN’s mission to advance Earth observation technologies for human prosperity by turning data into sustainable impact.
Cédric Léonard received an engineering degree in electronics from the National Institute for Applied Sciences (INSA) Rennes, France, and an M.Sc. degree in computer science (information technology) from Åbo Akademi University, Finland. He is currently pursuing a PhD at the German Aerospace Center (DLR) in collaboration with the Technical University of Munich (TUM), Germany. His research activities focus on Deep Learning for Earth Observation and Synthetic Aperture Radar (SAR), with particular interest in efficient AI approaches for onboard and edge computing, particularly FPGA-based implementations.
I am Gabriele Bertoli, a researcher in Artificial Intelligence, Hydrology, and Natural Hazard & Risk assessment, with a strong foundation in Civil and Environmental Engineering. My academic path began at Politecnico di Milano, where I completed my bachelor’s degree in Civil and Environmental Engineering. I then earned my master’s degree with honors in Geoengineering from the University of Florence. After a research fellowship focused on the flood risk of critical facilities, I started my international PhD in Civil and Environmental Engineering at the University of Florence, in co-tutelle with Technische Universität Braunschweig. During my doctoral studies, I also carried out a visiting period at Imperial College London within the Data Science Imperial, joining the Data Learning Group. I have submitted my PhD thesis entitled: “Flood Risk: An Interdisciplinary Approach Integrating Hydrology and Data Science”. I am currently working on Artificial Intelligence solutions for natural hazards and water resources management at University of Florence, and as a visiting researcher at ESA Φ-lab.
I’m a Master’s student in Aerospace Engineering at the University of Naples Federico II, where I previously earned my Bachelor’s degree in 2023. My strong passion and curiosity for space, science and innovation have always motivated me to seek out new experiences and challenges. In this context, I’m glad to be working on my Master’s thesis in collaboration with the ESA Φ-lab.
My research focuses on developing hardware-aware deep learning algorithms optimized for onboard processing of Synthetic Aperture Radar data in the range-compressed domain.
Throughout my academic journey, I’ve built a solid background in several areas of Space Engineering, from Mission Design and Systems Engineering to Remote Sensing, a field I quickly grew passionate about. With my current research, I aim to deepen my knowledge in AI4EO by exploring how Machine Learning and Deep Learning techniques can be used as powerful and efficient tools onboard satellites to accelerate SAR image processing and make it more accessible to end users, while maintaining high data quality.
