Google Earth Engine for Remote Sensing | Import, Upload & Export

Google Earth Engine (GEE) is a powerful cloud platform for geospatial analysis. In this guide, I’ll explain how to use it step by step, especially how to import and export data.

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🔐 1. Sign Up and Access Code Editor

• First, sign up here: https://earthengine.google.com/signup

• After approval, open the Code Editor:
  👉 https://code.earthengine.google.com/
  

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💻 2. Understand the Code Editor Interface

• Script Panel – Write your code

• Map Panel – View your outputs

• Console – Shows messages or errors

• Inspector – Click to inspect pixel values

• Assets Panel – Upload and access your files

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📥 3. How to Import Data in Google Earth Engine

A. Import Built-in Datasets (Like Landsat or Sentinel)

var image = ee.ImageCollection("COPERNICUS/S2")
              .filterBounds(ee.Geometry.Point(73.0551, 33.6844))
              .filterDate('2021-01-01', '2021-12-31')
              .median();

Map.centerObject(image, 9);
Map.addLayer(image, {bands: ['B4', 'B3', 'B2'], max: 3000}, 'Sentinel Image');

B. Upload Your Own Data (Shapefile or CSV)

1. In the Assets tab, click “NEW” > Table upload (for shapefile or CSV)

2. Upload your zipped shapefile (including .shp, .dbf, .shx, .prj)

3. After uploading, click “Import”

4. Use in code like this:

var shapefile = ee.FeatureCollection("users/your_username/your_asset_name");
Map.addLayer(shapefile, {}, "My Shapefile");

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💾 4. How to Export Data from GEE

A. Export Raster Image (GeoTIFF format)

Export.image.toDrive({
  image: image.select('B4'), // or NDVI, or any other band
  description: 'SentinelBand4',
  scale: 10,
  region: shapefile.geometry(),
  maxPixels: 1e13
});

B. Export Vector Data (Shapefile or CSV)

Export.table.toDrive({
  collection: shapefile,
  description: 'Exported_Shapefile',
  fileFormat: 'SHP' // or 'CSV'
});

🔔 Note: Your data will be saved to Google Drive, and you can download it from there.

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🧪 Bonus Example: NDVI Calculation & Export

// NDVI = (NIR - Red) / (NIR + Red)
var ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI');

Map.addLayer(ndvi, {min: 0, max: 1, palette: ['white', 'green']}, 'NDVI');

Export.image.toDrive({
  image: ndvi,
  description: 'NDVI_Image',
  scale: 10,
  region: shapefile.geometry(),
  maxPixels: 1e13
});

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📌 Summary Table

Task	Code/Method
Import satellite data	ee.ImageCollection()
Import shapefile/CSV	Upload to Assets
View on map	Map.addLayer()
Export raster	Export.image.toDrive()
Export shapefile/CSV	Export.table.toDrive()

Read What is Google Earth Engine for more information about this!🚀

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✅ Final Thoughts

Google Earth Engine makes remote sensing work easier and faster. With just a few lines of code, you can analyze massive datasets, visualize results, and export your outputs.

If you'd like a PDF guide, video tutorial, or full LULC classification workflow, just let me know in comment section—I’ll prepare it for you!