Emma Izquierdo-Verdiguier

Introducing our new Satellite Embedding dataset. Learn how to get the most out of this AI-powered Image Collection, featuring hands-on examples and real-world case studies.

Join us for an exciting session to learn how scientists, through cutting-edge earth observation data, cloud computing, and AI, are rewriting the playbook on scaling Nature-Based Solutions (NbS). In this session we will explore the journey from earth observation data to real-world impact.

Many would say that Land Classification is Earth Engine's raison d'etre. Having built a classifier, new users of Earth Engine are often amazed at their ability to pan and zoom across the entire earth's surface and comprehensively see the results of their model.

During this session, you'll hear 9 lightning talks featuring topics around monitoring and change detection in water resources and agricultural lands for sustainable use and conservation. Guillaume Attard, Ageoce Solutions, "Providing global climate change hazard information with its uncertainty - focus on groundwater recharge" Rodrigo Márquez, AgroSmartRobotics by ARVIPO, "Building a GEE database and it's ML processing capabilities for vineyards differentition in a farm cadastre" Hadi Jaafar, American University of Beirut, "AgSAT: Real-Time Irrigation Data at Your Fingertips" Puzhao Zhang, DHI

Many would say that Land Classification is Earth Engine's raison d'etre. Having built a classifier, new users of Earth Engine are often amazed at their ability to pan and zoom across the entire earth's surface and comprehensively see the results of their model.

This session will cover the basics of using traditional ML methods in the Earth Engine code editor, including building, training, using and validating classification models, as well as some advanced techniques for improving accuracy and uncertainty, looking at the temporal, spatial and spectral domains. It will also cover some methods for change detection.