Linda Gulbe has a doctoral degree in computer science and informatics. Her thesis, defended in 2020 in the University of Latvia, was focused on tree crown coverage mapping using lidar data and aerial photography.
Since 2019, Linda has been involved in various remote sensing-related projects at the EDI (Institute of Electronics and Computer Science) and her research interests and experience include deep learning, image processing algorithms, forest remote sensing and preparing specialized data processing workflows.
Recent projects
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Tālizpēte un mašīnmācīšanās purvu biotopu uzraudzībai (PurvEO)
- Remote sensing based system for forest risk factor monitoring (Forest Risk) #ESIF
- Sentinel for confidence in outdated maps: SentiMap #ESIF
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Object based context aware self-learning network for land cover classification (Dynland-2) #ESIF