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. ------------------------------------------------------------------------------------ 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. ----------------------------------------------------------------------------------- Link to downloading code: https://drive.google.com/file/d/1sDVt... ----------------------------------------------------------------------------------- Supervised Image Classification of Sentinel-2A Imagery in GEE - Part 1 Video Link:    • Supervised Image Classification of Sentine...   Supervised Image Classification of Sentinel-2A Imagery in GEE - Part 2 Video Link:    • Supervised Image Classification of Sentine...   ---------------------------------------------------------------------------------- Join this channel to get access to perks:    / @terraspatial   #sentinel2 #googleearthengine #remotesensing #imageclassification #supervisedlearning #machinelearning #LandCoverMapping #VegetationMonitoring #UrbanPlanning #RandomForestClassifier #JavaScriptAPI #StepByStepTutorial #DataPreprocessing #TrainingSamples #FeatureCollection #Tutorial #ESA #EuropeanSpaceAgency #OpenData #EarthObservation #DataScience #GIS #Geospatial #dataanalysis #LULC #supervisedclassification