High resolution satellite products provide detailed snapshots of the Earth’s surface. In this working group, our community members examined the capabilities multiple ML techniques trained on Sentinel-2 data to develop accurate and robust land cover classification algorithm across Canada.
Dr Karen Smith | Advisor ~ Assistant Professor, U of Toronto
Dr Andre Erler | Advisor ~ Senior Climate Scientist at Aquanty
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Land Cover Classification Using Sentinel-2 - RECIPE | Aggregate Intellect
Objective: Develop an accurate and robust classifier that takes as input 12 spectral bands from Sentinel-2 L2A (the raw bands, no necessary preprocessing) and predicts a set of 19 NRCan 2015 land cover classes, at a resolution of 60 m.;
Others who reached the minimal goal
Team Jungle: Sajad yazdanparast, Afshin Amini
Team JP: Paola Andrea, Juan Manuel
Due to the generosity of our sponsor, we were able to give over $4000 in gifts & prizes to participants, leads & advisors.