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Updated in [July 25th, 2023]
This Google Earth Engine Mega Course: Remote Sensing & GIS is the only course needed to learn to code and become an Earth Engine expert. With an average rating of 4.8, it is one of the highest-rated courses available online. At 12+ hours, this course is the most comprehensive Google Earth Engine course available. Even those with no programming experience can take this course and go from beginner to mastery. The course is taught by an experienced spatial data scientist and former NASA fellow, and has been updated to be 2023-ready. The curriculum was developed over four years, with comprehensive student testing and feedback. Over 20,000 students have been taught how to code and apply spatial data science and cloud computing. The course is constantly updated with new content, projects, and modules, and includes example data and sample scripts. Topics covered include: Introduction to Earth Engine JavaScript API, Explore Earth Engine, Sign Up with Earth Engine, Basic JavaScript Syntax, Sources of Earth Observation Data, Landsat Image Visualization, Mathematical Operations with Images, Image Collection Metadata, Filtering Image Collection, Mapping Image Collection, Reducing Image Collections, Earth Engine Feature Collections, Earth Engine Geometries, Geometric Operations, Mapping Feature Collections, Reducing Feature Collections, Raster to Vector Conversion, Vector to Raster Conversion, Time Series Charts, Histograms, Export Images, Image compositing, Image convolutions, Image mosaicing, Satellite data summary, Remote sensing for land cover mapping, Remote sensing for water resources, Remote sensing for forest mapping, and Machine learning with satellite data. The course includes over 12 hours of HD video tutorials, taking students step-by-step through engaging video tutorials and teaching them everything they need to know to succeed as a spatial data scientist and Earth Engine expert. Click the buy now button and join the highest-rated Google Earth Engine course.
Course Syllabus
Explore Earth Engine API
Earth Engine Code Editor
Images
Geospatial Data Sources
Image Collections
Vector Calculations
Explore Reducers
Visualization of Geospatial Data
Export Geospatial Data
Digital Image Processing
Satellite Data Summary
Land Use Land Cover Products
Water Resources Application
Public Health Application: Human Population
Public Health Application: Forest Fire Mapping
Public Health Application: Air Pollution
Forest Application: Global Forest Cover Mapping
Forest Application: National Forest Cover Mapping
Forest Application: Other Forest Indices
Machine Learning: Unsupervised Classification
Machine Learning: Training Data
Machine Learning: Supervised Classification (CART)
Machine Learning: Supervised Classification (Random Forest)
Final Project
Bonus Lectures
