Machine learning (ML) based Image Classification in Google Earth Engine (GEE) using Sentinel-2 data

In this comprehensive tutorial, we delve into the realm of Machine Learning (ML) applied to satellite image classification, specifically focusing on identifying crops using Sentinel-2 satellite imagery and Google Earth Engine (GEE) platform. With step-by-step guidance, you'll learn how to use temporal Sentinel-2 images to train random Forest model and evaluate their performance for accurate crop classification. Whether you're a beginner or an experienced practitioner, this video equips you with the knowledge and tools to harness the power of ML for agricultural insights using Sentinel-2 (10m) data in Google Earth engine (GEE). Don't miss out on this essential resource for advancing your skills in remote sensing and crop monitoring!

Google Earth Engine (GEE) is a powerful cloud-based platform developed by Google for planetary-scale environmental data analysis. It combines a multi-petabyte archive of satellite imagery and geospatial datasets with an array of analysis capabilities, all accessible through a web-based interface and a JavaScript API.

Sentinel-2 is a satellite mission developed by the European Space Agency (ESA) as part of the Copernicus Programme. It consists of a constellation of two identical satellites, Sentinel-2A and Sentinel-2B, launched in 2015 and 2017 respectively. The primary objective of the Sentinel-2 mission is to provide high-resolution (10m) optical imagery of the Earth's land and coastal regions for various applications such as land cover monitoring, agriculture, forestry, urban planning, and disaster management.

GEE Script used in the video:
https://code.earthengine.google.com/7...