Automated Shoreline Extraction Using Landsat-9 & Sentinel-2 in Google Earth Engine - Python & Colab

Описание: In this tutorial, you’ll learn how to automatically extract coastlines and shorelines using the Normalized Difference Water Index (NDWI) from Landsat-9 and Sentinel-2A imagery — all processed efficiently in Google Earth Engine (GEE) with Python on Google Colab. This workflow is ideal for coastal monitoring, erosion studies, sea-level change analysis, and GIS-based shoreline mapping.
What you’ll learn in this video
Setting up Google Colab + GEE High-Volume API, interactive ROI selection using Geemap, NDWI computation, creating cloud-filtered median composites, converting Earth Engine images to Xarray datasets, generating binary water masks (Water vs Land), applying morphological erosion to refine water bodies, automated shoreline extraction from NDWI results, comparing Landsat-9 vs Sentinel-2A shorelines, visualizing NDWI, water masks, and extracted coastlines
Who should watch this?
Remote sensing & GIS students, coastal and environmental researchers, python users learning GEE workflows, scientists working on shoreline change & erosion, anyone interested in automated satellite image analysis
Downloadable Code link: https://drive.google.com/file/d/1EdWV...
Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A (SR): https://developers.google.com/earth-e...
USGS Landsat 9 Level 2, Collection 2, Tier 1: https://developers.google.com/earth-e...
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💬 Comment below if you want this converted into a vector shoreline (Shapefile/GeoJSON) or extended for time-series analysis.
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#ShorelineExtraction #NDWI #Landsat9 #Sentinel2 #GoogleEarthEngine #PythonGIS #RemoteSensing #CoastalMonitoring #Geemap #Xarray #GEE