Emma Izquierdo-Verdiguier

Research Focus

I work on developing machine learning algorithms for remote sensing data analysis. My research interests include using Earth Observation data and cloud computing environments for land surface phenology. My previous research focused on nonlinear feature extraction based on kernel methods and automatic object identification, and multispectral image classification.
After I finished my university degree, I devoted four years to research in photo-interpreting and GIS data, giving professional advice and technical support. In addition, I developed a spectral-spatial feature extraction methodology in my MSc. thesis to detect and classify growing tree plantations automatically. Subsequently, my Ph.D. research was devoted to nonlinear feature extraction techniques for remote sensing data analysis. In the context of that research, I created toolboxes (written in Matlab) of algorithms for information extraction. And after my Ph.D., I gained expertise with Google Earth Engine using JavaScript and Python and have also leveraged cloud computing techniques to develop the processing chain for classifying high spatial resolution images.