Remote Sensing with Sentinel-2 Satellite Images | Sponsored by the Government of Canada (NRCan)

The Problem

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.

Working Group Leads / Advisors

~ Fraser King | Lead PhD.c @ University of Waterloo, Canada

Dr Karen Smith | Advisor ~ Assistant Professor, U of Toronto

Dr Andre Erler | Advisor ~ Senior Climate Scientist at Aquanty

Project Recipe

At Aggregate Intellect, we’re contextualizing the world’s technical knowledge! Find below the 25+ resources used to build out this project - contextualized & commented. If you felt it empowered you to build using cool new tech, thank us by telling a friend!

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.;

https://ai.science/l/e398164b-93ad-46de-a9d9-1b9a0324d3f4