Remote Sensing, GIS and Google Earth Engine – Two-Week Applied Course

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About Course

This fourteen-day professional training programme provides comprehensive practical knowledge of Remote Sensing, GIS, and cloud-based geospatial processing using Google Earth Engine (GEE).

Participants will learn the principles of satellite remote sensing, geospatial data management, satellite image preprocessing, vegetation indices, land-use/land-cover mapping, and spatial analysis. Special emphasis will be placed on Google Earth Engine, enabling participants to process large volumes of satellite imagery without requiring high-performance local computing infrastructure.

The course introduces JavaScript programming in GEE, satellite image collections, cloud masking, image compositing, spectral indices, supervised classification, and geospatial visualization. Participants will complete a practical project using real satellite datasets.

What Will You Learn?

  • Process Landsat, Sentinel-1 and Sentinel-2 datasets.
  • Perform geospatial analysis using QGIS and Google Earth Engine.
  • Develop basic JavaScript scripts for geospatial processing.
  • Calculate and interpret vegetation and water indices.
  • Perform supervised land-use/land-cover classification.
  • Conduct basic change detection and accuracy assessment.
  • Export and visualize geospatial results.

Course Content

Topic 1: Foundation to the Remote Sensing
This fourteen-day professional training programme provides comprehensive practical knowledge of Remote Sensing.

  • Day 1: Remote Sensing Fundamentals
  • Day 2: Satellite Data and Missions

Topic 2: GIS and Spatial Analysis
Participants will learn the principles of satellite remote sensing, geospatial data management, satellite image preprocessing, vegetation indices, land-use/land-cover mapping, and spatial analysis.

Topic 3: Spatial Analysis on Cloud
Special emphasis will be placed on Google Earth Engine, enabling participants to process large volumes of satellite imagery without requiring high-performance local computing infrastructure.

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