Gonzalo Mateo García

I’m a researcher in UNEP International Methane Emissions Observatory. I collaborate with the Image and Signal Processing Group at Universidad de Valencia with Prof. Luis Gómez-Chova.

My research now is focused on methane detection from remote sensing imagery (multispectral and hyperspectral). In the past, I have conducted research in the intersection of machine learning and the Earth sciences. I have developed machine learning models for methane plume segmentation, super-resolution, domain adaptation, change detection, cloud detection, flood segmentation, crop yield estimation and also for weather and energy forecasting. We’ve even deployed one of these models onboard a satellite.

2023

I’ve joined UNEP IMEO to develop the Methane Alert and Response System.

2022

I led two research projects on methane detection and atmospheric correction emulation in the context of ESA Cognitive Cloud Computing in Space initiative. This call explored machine learning applications to make satellites more reactive, agile and autonomous.

We produced floodmaps using ml4floods to respond to the Australian floods that occurred in February and April this year. This visualization shows the floodmaps over West coast of Australia.

I handed-in my PhD Thesis: Transfer learning of Deep Learning Models for Cloud Detection in Optical Satellite Images.

I participated in the development of CloudSEN12 an open-sourced global dataset and a model for cloud and cloud shadow masking in Sentinel-2. See CloudSEN12 in the Google Earth Engine. This work is published in Scientific Data.

2021

I led ml4floods, an initiative of Trillium funded by the United Kingdom Space Agency (UKSA) to develop tools for end-to-end flood mapping. We created a python package with deep learning trained models for flood detection in Sentinel-2 and Landsat imagery.

We ran a set of experiments onboard D-Orbit’s ION satellite to demonstrate an ML payload. ESA Press release here. The payload was able to reduce downlink latency, adapt to different optical instruments and be updated directly in space. Pre-print of the work submitted to Scientific Reports available here.

In participated in an Frontier Development Lab Europe winter research sprint working on multi-image super resolution for Sentinel-2. The outcome of this work is published in the ISPRS Journal of Photogrametry and Remote Sensing.

I participated as a mentor in Frontier Development Lab Europe research sprint working on onboard unsupervised change detection. This work is published in Scientific Reports.

2020

I participated as a researcher in the NASA Frontier Development Lab research sprint working on monitoring water on small streams. This project was funded by NASA and the United States Geological Survey (USGS).

I participated in the Cloud Masking Intercomparison eXcercise organized by the Commitee on Earth Observation Satellites (CEOS) and sponsored by ESA and NASA. The results of this study are published in Remote Sensing of Environment.

Our work in adversarial domain adaptation for satellite images was published in IEEE JSTARS journal. This video shows some images of the model in action.

2019

I participated as a researcher in the Frontier Development Lab 2019 Europe research sprint working with the Disaster Prevention, Progress and Response team on onboard flood segmentation. The results of this sprint are published in Nature Scientific Reports.

Our work in transfer learning for cloud detection between Landsat-8 and Proba-V was published in the ISPRS Journal of Photogrammetry and Remote Sensing.

2018

We developed the operational cloud detection algorithm for Proba-V that will be run on the Proba-V archive in the C2 reprocessing.

2017

I worked under a Google Earth Engine Award project developing machine learning cloud detection algorithms. We published our results in Remote Sensing. This is a nice demo with some results.

Our ML based model for detecting clouds in Proba-V images ended up in second position in the ESA Proba-V Round Robin Exercise only 0.1% less accurate than the leading solution. Results of the comparison exercise were published the Multitemp conference.

2012 - 2016

I worked for several years as a software engineer in renewable energy forecasting at MeteoLogica.

Please feel free to contact me at: