Information
📚 Organization | Tracasa Instrumental S.L. |
📆 Period @ ESA Φ-lab | January 2026 - April 2026 |
🌍 Project @ ESA CIN | ⚠️ Project is ongoing - Foundational Model for Multi-Frequency SAR |
🔨 Linkedin | https://www.linkedin.com/in/jfamieva/ |
📝 Publications | Google Scholar |
Bio
Juan Francisco Amieva is a researcher in Earth Observation and Artificial Intelligence, currently pursuing an industrial PhD in AI jointly between Tracasa Instrumental and the Public University of Navarre (UPNA). His work focuses on deep learning methods for Synthetic Aperture Radar (SAR) image super-resolution, and during his visit at ESA Φ-lab he contributes to the development of a multi-frequency SAR foundational model for large-scale forest characterisation. He holds an MSc in Geoinformatics Engineering (with honours) from Politecnico di Milano, a Master in Data Intelligence (Big Data orientation) from the National University of La Plata, a Specialisation 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, he has worked at Tracasa Instrumental S.L., developing AI-driven solutions that combine satellite data analysis with geospatial intelligence.
Current Role
Juan currently serves as a Data Scientist in Earth Observation at Tracasa Instrumental (Spain), where he develops deep learning models for remote sensing, with a particular focus on SAR image enhancement.
Areas of Expertise
His expertise lies in integrating Earth Observation and Artificial Intelligence for satellite data analysis. Juan works with SAR, multispectral, and hyperspectral imagery, developing deep learning models for applications such as chlorophyll-a estimation in lakes, crop-yield prediction, density estimation, object detection, super-resolution, and change detection. This combination of EO data and AI methods enables him to design scalable and transferable models capable of extracting meaningful information from complex datasets, aligning closely with ESA Φ-lab CIN’s mission to advance next-generation geospatial modelling.
Vision for the Future
Juan aims to contribute to the development of foundational models for Earth Observation that enhance large-scale monitoring and understanding of forest ecosystems. His long-term goal is to support the creation of generalised and robust AI approaches that strengthen forest characterisation, environmental monitoring, and climate resilience, in line with ESA Φ-lab’s commitment to advancing EO innovation.