María Piles
Also known as Maria Piles
María Piles is a remote sensing researcher who applies machine learning to Earth observation. She earned her doctorate in 2010 and worked in Melbourne, Barcelona and Valencia. Her work connects the SMOS and SMAP satellite missions with research on soil moisture and vegetation. She studies models that respect physical laws, causal inference and interpretable features. Her projects include national crop-yield forecasting, explainable AI for Copernicus data and machine learning for Earth-system processes and extremes.
- Now at
- Image and Signal Processing group
- Works
- 256
- Citations
- 11,090
- h-index
- 37
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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2005–2010
Train in remote sensing
Piles received a telecommunications engineering master’s degree in Valencia in 2005 and a doctorate at the Universitat Politècnica de Catalunya in 2010. She then joined the University of Melbourne as a research fellow.
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2011–2017
Work with Earth-observation measurements
She researched at UPC from 2011 to 2016 and held an MIT research affiliation. After work at the Institute of Marine Sciences, she joined Valencia’s Image and Signal Processing group in 2017. Her research emphasizes soil moisture and vegetation measurements.
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2021–2023
Forecast crops and explain satellite models
In 2021, she worked on a machine learning framework for national crop-yield forecasts using ASAP and FAOSTAT data. She also worked on DeepCube, a 2021–2023 project on explainable AI pipelines for Copernicus satellite data.
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2024–2028
Model Earth-system processes
Piles participates in AI4PEX, a project on AI and machine learning for Earth-system processes and extremes. Her research interests include physical constraints, causal inference and understandable feature representations.
Sources and credits
Photo: ELLIS profile; creator not stated. License: Not stated by source. Source: ellis.eu. Cropped to a square and resized. Ask us to remove or replace this photo.