4.86
(7 Ratings)

Advanced Geospatial Analysis with Google Earth Engine and Python for GeoAI

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About Course

This intensive one-month professional training programme focuses on advanced geospatial analysis, satellite data processing, Python-based geospatial automation, and practical applications of GeoAI.

The course is designed for learners who want to move beyond conventional GIS operations and develop computational skills for handling large-scale geospatial datasets. Participants will gain practical experience in Google Earth Engine, Python-based raster and vector processing, satellite time-series analysis, machine learning, and introductory deep learning applications.

The programme covers advanced workflows involving Sentinel-1, Sentinel-2, Landsat and other Earth observation datasets. Participants will learn to automate geospatial analysis, extract environmental indicators, perform land-cover classification, assess model accuracy, and develop introductory machine learning and deep learning workflows for geospatial applications.

The course culminates in an independent capstone project addressing a real-world environmental or geospatial problem.

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What Will You Learn?

  • Process and analyse optical and microwave satellite datasets.
  • Perform advanced GIS operations and geospatial analysis.
  • Develop Google Earth Engine scripts for satellite data processing and automation.
  • Use Python for raster, vector and numerical geospatial analysis.
  • Apply machine learning techniques to geospatial classification and prediction.
  • Understand introductory deep learning and GeoAI workflows.
  • Develop independent geospatial research and analytical projects.
  • Prepare maps, visualisations, scripts and reproducible project outputs.

Course Content

Module 1: Advanced Remote Sensing and GIS (Days 1–5)
This intensive one-month professional training programme focuses on advanced geospatial analysis, satellite data processing, Python-based geospatial automation, and practical applications of GeoAI.

  • Day 1 – Advanced Principles of Remote Sensing and Earth Observation
  • Day 2 – Satellite Data Sources and Earth Observation Platforms
  • Day 3 – Advanced GIS and Geospatial Data Management
  • Day 4 – Satellite Image Preprocessing and Enhancement Techniques
  • Day 5 – Digital Elevation Models and Terrain-Based Environmental Analysis

Module 2: Advanced Google Earth Engine (Days 6–12)
The course is designed for learners who want to move beyond conventional GIS operations and develop computational skills for handling large-scale geospatial datasets. Participants will gain practical experience in Google Earth Engine, Python-based raster and vector processing, satellite time-series analysis, machine learning, and introductory deep learning applications.

Module 3: Python Programming and Geospatial Data Science (Days 13–19)
The programme covers advanced workflows involving Sentinel-1, Sentinel-2, Landsat and other Earth observation datasets. Participants will learn to automate geospatial analysis, extract environmental indicators, perform land-cover classification, assess model accuracy, and develop introductory machine learning and deep learning workflows for geospatial applications.

Module 4: Machine Learning and Introductory GeoAI (Days 20–25)

Module 5: Independent Geospatial Project and Capstone Assessment (Days 26–30)

Student Ratings & Reviews

4.9
Total 7 Ratings
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4
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3 years ago
this is so beautyful course
3 years ago
Very Nice course
3 years ago
Completed
3 years ago
this is the best course ever
3 years ago
Nice
7 years ago
The course is extraordinary!!
It explains everything from A to Z regarding Nutrition and also there are some very valuable workout tips.
Great job!
7 years ago
Absolutely fantastic!! Thanks so, so much Felix for your concise, practically useful and well informed course.