The Google Earth Engine Mega Course: Remote Sensing & GIS
What you'll learn
- Students will access and sign up the Google Earth Engine platform
- Download, process and visualize various satellite data including Landsat, MODIS, Sentinel and VIIRS
- Apply GIS techniques to process and analyze various vector data
- Generate various visualizations including time series and histogram charts from remote sensing data
- Export various vector data including KML and CSV files
- Export images, charts and videos
- Learn to perform various image processing including mosaiccing, compositing, zonal statistics, and neighborhood analysis
- Classification of satellite data with Machine learning
- Complete a final GIS project on downloading, processing, analyzing and visualizing big data
- This course has no requirements.
Welcome to the Google Earth Engine Mega Course: Remote Sensing & GIS, the only course you need to learn to code and become an Earth Engine expert. With a 4.5 average rating, my Earth Engine course is one of the highest-rated courses.
At 7+ hours, this Earth Engine course is without a doubt the most comprehensive Google Earth Engine course available online. Even if you have zero programming experience, this course will take you from beginner to mastery.
The course is taught by an experienced spatial data scientist and former NASA fellow.
The course has been updated to be 2023-ready and you'll be learning the latest tools available on the cloud.
The curriculum was developed over four years, with comprehensive student testing and feedback.
We've taught over 20,000 students how to code and apply spatial data science and cloud computing.
The course is constantly updated with new content, with new projects, and modules.
You will have access to example data and sample scripts.
In this course, we will cover the following topics:
Explore Earth Engine
Sign Up with Earth Engine
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
Mapping Feature Collections
Reducing Feature Collections
Raster to Vector Conversion
Vector to Raster Conversion
Time Series Charts
Satellite data summary
Remote sensing for water resources
Remote sensing for forest mapping
Machine learning with satellite data
The course includes over 7 hours of HD video tutorials. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to succeed as a spatial data scientist and Earth Engine expert.
So, what are you waiting for? Enroll now and join the highest-rated Google Earth Engine course.
Who this course is for:
- This course is meant for professionals who want to harness the power Google Earth Engine cloud computing platform.
- People who want to understand various satellite image processing techniques.
- Anyone who wants to learn spatial analysis on the cloud.
- People who ware working with satellite remote sensing data such as Landsat, MODIS, Sentinel-2, and VIIRS.
- Anyone who wants to apply for GIS or Remote Sensing Specialist job position.
Spatial eLearning provides online courses teaching remote sensing, GIS, machine learning, cloud computing, and spatial data science skills. Our mission is to make highly valuable geospatial data science skills accessible and affordable to anyone and anywhere around the world. We teach 20,000 plus students in over 170 countries around the world. Spatial eLearning’s valuable learning resources include webinars, books, free tutorials, and online courses.
I am a geospatial data scientist with 15-plus years of experience. I am a former NASA Earth and Space Science fellow. My research interests include remote sensing, big data, and environmental change. More specifically, I am interested in applying big geospatial data, cloud computing, and machine learning to solve complex environmental problems, especially land cover change, climate change, water resources, agriculture, and public health.