Feb 27 • 30M

Satellite image time series with Gilberto Camara

SITS is an open-source R package for land use and land cover classification of big Earth observation data

 
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Dive into the world of deep learning for satellite images with your host, Robin Cole. Robin meets with experts in the field to discuss their research, products, and careers in the space of satellite image deep learning. Stay up to date on the latest trends and advancements in the industry - whether you’re an expert in the field or just starting to learn about satellite image deep learning, this a podcast for you. Head to https://www.satellite-image-deep-learning.com/ to learn more about this fascinating domain
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In this episode, Robin catches up with Gilberto Camara to talk about SITS. SITS is an open-source R package for land use and land cover classification of big Earth observation data using satellite image time series. Gilberto is a Senior Researcher in GIScience, Geoinformatics, Spatial Data Science and Land Use Change at Brazil’s National Institute for Space Research.

Bio: Prof. Dr. Gilberto Câmara is a Brazilian researcher in Geoinformatics, GIScience, Spatial Analysis, and Land Use Modelling, who works at Brazil's National Institute for Space Research (INPE). He is internationally recognized for promoting free access for geospatial data and for setting up an efficient satellite monitoring of the Brazilian Amazon rainforest. After retiring from INPE in June 2016 after 35 years of work, he continues to conduct R&D activities at INPE as a Senior Research Fellow.

Logo animation and thumbnail credits: Mikolaj Czerkawski @mikonvergence