In this tutorial, we will learn how to perform supervised image classification of Sentinel2 imagery using Google Earth Engine. Sentinel2 is a satellite mission from the European Space Agency that provides highresolution optical images of the Earth's surface, which can be used for various applications such as land cover mapping, vegetation monitoring, and urban planning.

Supervised image classification is a machine learning technique that allows us to automatically classify pixels in an image into different land cover classes based on training data. In this tutorial, we will use a random forest classifier to classify Sentinel2 imagery into four land cover classes: water, urban, vegetation, and barren.

Link to downloading code: https://drive.google.com/file/d/1sDVt...

Supervised Image Classification of Sentinel2A Imagery in GEE Part 1 Video Link:
   • Supervised Image Classification of Se...  
Supervised Image Classification of Sentinel2A Imagery in GEE Part 2 Video Link:
   • Supervised Image Classification of Se...  

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