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Mastering Earth Observation with AI & Google Earth Engine
Rating: 4.4 out of 5(62 ratings)
316 students

Mastering Earth Observation with AI & Google Earth Engine

Level up your satellite remote sensing skill with Google Earth Engine and Deep Learning (Artificial Intelligence)
Created byTek Kshetri
Last updated 4/2025
English

What you'll learn

  • Google Earth Engine
  • Remote Sensing/Earth Observation
  • Time Series Raster Analysis
  • Change Detection Analysis
  • Helpful Ideas for Semester Projects
  • Multiple ways to prepare LULC maps in cloud environment
  • Landslide Susceptibility Mapping
  • Forest Fire Mapping
  • Flood Analysis and Exposure Calculation
  • Work on Real Projects
  • Geospatial Analysis

Course content

12 sections30 lectures7h 40m total length
  • Introduction5:03
  • Lecture Outline3:11
  • Remote sensing basics21:21
  • How to download and run code in your laptop6:19
  • Resources and how to get help0:29

Requirements

  • Basic knowledge of remote sensing
  • Intermediate knowledge of deep learning

Description

Harness the power of Google Earth Engine (GEE) and Artificial Intelligence (AI) to analyze satellite imagery and monitor environmental changes. This comprehensive course will guide you through the fundamentals of remote sensing, machine learning, and deep learning for geospatial analysis.


You will start with an introduction to GEE and remote sensing, learning how to access and process satellite imagery. From there, you’ll dive into Land Use and Land Cover (LULC) mapping, applying machine learning and deep learning techniques to classify landscapes effectively.


The course also covers time-series image visualization, allowing you to create animated representations of changes over time. You’ll explore real-world environmental applications, including forest fire mapping, flood analysis using multiple satellite datasets, and landslide susceptibility mapping. Additionally, you will learn change detection analysis using deep learning, a crucial technique for tracking landscape modifications.


By the end of this course, you will have the skills to:


  • Utilize GEE for satellite image processing and visualization

  • Implement machine learning and deep learning for LULC classification

  • Conduct disaster mapping for floods, forest fires, and landslides

  • Apply change detection techniques to monitor environmental transformations


Whether you are a researcher, student, or GIS professional, this course will equip you with practical skills to analyze Earth’s dynamic surface using cutting-edge geospatial technologies.

Who this course is for:

  • Students & Researchers in GIS, Remote Sensing, and Environmental Science
  • Data Scientists looking to expand their geospatial analysis skills
  • GIS Professionals working with Earth Observation and AI-based mapping
  • Disaster Management Experts interested in satellite-based risk assessment
  • Developers & Enthusiasts eager to explore Machine Learning and Deep Learning in geospatial applications