Supervised Image Classification of Sentinel-2A Imagery in Google Earth Engine | Part - 1

In this tutorial, we will learn how to perform supervised image classification of Sentinel-2 imagery using Google Earth Engine. Sentinel-2 is a satellite mission from the European Space Agency that provides high-resolution optical images of the Earth's surface, which can be used for various applications such as land cover mapping, vegetation monitoring, and urban planning.
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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 Sentinel-2 imagery into four land cover classes: water, urban, vegetation, and barren.
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Link to downloading code: drive.google.com/file/d/1sDVt...
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Supervised Image Classification of Sentinel-2A Imagery in GEE - Part 1 Video Link:
• Supervised Image Class...
Supervised Image Classification of Sentinel-2A Imagery in GEE - Part 2 Video Link:
• Supervised Image Class...
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[@davidshaw9139]

Very good video sir

[@TerraSpatial]

Thanks a lot Mr. David

[@gamerchan8754]

Excellent 👍👌

[@TerraSpatial]

Thanks Mr.Chan

[@carolinrock-yn5ic]

Thanks for video and code

[@TerraSpatial]

Welcome

[@gamerchan8754]

Thanks for the code

[@TerraSpatial]

Your are welcome Mr. Chan

[@nasreensultana1363]

Thank for this video.. bless u..

[@TerraSpatial]

Hi Ms. Nasreen Sultana, Thanks for your valuable comment