Luis Gomez-Chova

Luis Gomez-Chova is a Full Professor at the Electronic Engineering Department of the University of Valencia, Spain, and a Senior Researcher at the Image and Signal Processing Group (ISP) within the Imaging Processing Laboratory (IPL). His roles include teaching Digital Electronics, Digital Signal Processing, and Machine Learning and Image Processing for Remote Sensing. He has held visiting researcher positions at institutions such as ESA-ESRIN (Italy), DLR-DFD (Germany), and DTU Space (Denmark).

His research focuses on machine learning and signal/image processing applied to remote sensing data analysis, with a particular emphasis on cloud detection, multispectral image processing, and Earth observation. He leads projects funded by the Spanish Ministry of Science and Innovation, European Space Agency (ESA), and other international bodies. Notable projects include CH4AI (methane plume detection), DEEPCLOUD (cloud detection algorithms), and TECMAR (maritime activity monitoring).

Gomez-Chova has received multiple awards, including the IEEE Senior Member distinction (2015), the 2013 IEEE GRS Letters Prize Paper Award, and the 2008 European Best PhD Thesis Award in Geoscience and Remote Sensing. His work has been supported by grants like the Google Earth Engine Research Award (2015) and Spanish Ministry grants such as PID2023-148485OB-C21.

He has advised PhD students including Julia Amorós-López, Emma Izquierdo-Verdiguier, and Gonzalo Mateo-García. His research group is involved in developing advanced algorithms for satellite image processing, cloud masking, and environmental monitoring. Key collaborations include work with the European Space Agency on projects like OpenSR and Proba-V cloud detection.

Gomez-Chova’s contributions extend to open-source datasets like CloudSEN12 and the development of benchmark frameworks for cloud detection. He actively publishes in top journals like IEEE Transactions on Geoscience and Remote Sensing and conferences such as IGARSS. His lab, part of the ISP group, emphasizes both theoretical advancements and applied solutions in remote sensing analytics.