Satellite image deep learning

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.* https://github.com/e-sensing/sits * https://gilbertocamara.org/* Video on YouTubeBio: 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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com

In this career chat, Robin catches up with Nishant Yadav to hear about his path from PhD to Applied Scientist (II) at Microsoft Azure AI working on computer vision. Nishant graduated from Northeastern University in Boston, US, with a Ph.D. in machine learning with applications in environmental and climate science. His research focused on developing deep transfer learning methods for extracting information from remotely-sensed data (e.g., satellite imagery). Nishant is an AI optimist, and his current favourite hobby is learning more about generative AI. * https://www.linkedin.com/in/nisyad/* 📺 Video of this chat on YouTube This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com

In this episode, Robin catches up with Michail Tarasiou to discuss the new paper, ViTs for SITS: Vision Transformers for Satellite Image Time Series. This paper introduces the temporo-spatial vision transformer (TSViT) architecture. The TSViT incorporates novel design choices that make it suitable for time series tasks such as crop classification. In this work, TSViT crop classification and segmentation models are trained and evaluated on Sentinel 2 datasets and achieve state of the art (SOTA) results on these tasks by a significant margin. This is an exciting step towards high accuracy and low cost & automated crop mapping using remote sensing imagery.Paper authors: Michail Tarasiou, Erik Chavez, Stefanos Zafeiriou* 📖 Paper* 💻 Code on Github* 📘 Transformers in remote sensing blog post* 👤 Michail on LinkedIn This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com

In this episode of the career chat series, Robin catches up with Zhuang-Fang NaNa Yi to hear about her career path into the role of senior machine learning engineer at Regrow Ag* https://www.linkedin.com/in/zhuang-fang-yi-phd-01178a34/* https://www.regrow.ag/Bio: Zhuang-Fang NaNa Yi is a senior machine learning engineer at Regrow Ag. Her day-to-day work involves building R&D and machine learning models to scale and generate accurate machine learning-derived data layers for sustainable and regenerative agriculture at Regrow. Formerly, she was a machine learning engineer & GeoAI team lead at Development Seed and a research scientist at World Agroforestry Centre. She had a Ph.D. in Ecology from the Chinese Academy of Sciences and a B.S. in Geography from Sun Yat-Sen University. Outside of work, she is an artist, and you can often find her work at local art galleries, art shows, and art centres in the DC area. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com

In this podcast, Robin catches up with Philip Robinson to discuss his career path, and hear how he transitioned from working in computer security research, to working on environmental and satellite imaging challenges at the Global Fishing Watch. - https://www.linkedin.com/in/philip-robinson-2878642a/- https://globalfishingwatch.org/ Bio: Philip Robinson is a Scientific Programmer at Global Fishing Watch. Global Fishing Watch works to increase transparency of human activity at sea, by enabling scientific research in how we manage our ocean. Philip transitioned his career from computer security research to environmental and satellite imaging work. His masters studies were in deep learning for marine acoustic anomaly detection, and he is particularly interested in environmental auditing and citizen science problems. Logo and thumbnail credits: Mikolaj Czerkawski @mikonvergence This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com