Introduction to Google Earth Engine (GEE) - AulaGEO
Get started with geospatial data by applying the JavaScript programming language
Updated on Jun, 2025
Language - English
Development ,Web Development,JavaScript
Lectures -7
Resources -6
Duration -1.5 hours
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Course Description
Google Earth Engine is a platform for scientific analysis and visualization of geospatial datasets, for academic, non-profit, business and government users.
Google Earth Engine hosts satellite imagery and stores it in a public data archive that includes historical earth images going back more than forty years. The images, ingested on a daily basis, are then made available for global-scale data mining.
Earth Engine also provides APIs and other tools to enable the analysis of large datasets.
Google Earth enables you to travel, explore, and learn about the world by interacting with a virtual globe. You can view satellite imagery, maps, terrain, 3D buildings, and much more.
Earth Engine, on the other hand, is a tool for analyzing geospatial information. You can analyze forest and water coverage, land use change, or assess the health of agricultural fields, among many other possible analyses.
While the two tools rely on some of the same data, only some of Google Earth's imagery and data is available for analysis in Earth Engine.
#AulaGEO
In this course you will learn:
Google Earth Engine hosts satellite imagery and stores it in a public data archive that includes historical earth images going back more than forty years. The images, ingested on a daily basis, are then made available for global-scale data mining.
Earth Engine also provides APIs and other tools to enable the analysis of large datasets.
Google Earth enables you to travel, explore, and learn about the world by interacting with a virtual globe. You can view satellite imagery, maps, terrain, 3D buildings, and much more.
Earth Engine, on the other hand, is a tool for analyzing geospatial information. You can analyze forest and water coverage, land use change, or assess the health of agricultural fields, among many other possible analyses.
While the two tools rely on some of the same data, only some of Google Earth's imagery and data is available for analysis in Earth Engine.
#AulaGEO
In this course you will learn:
Google Earth Engine Course overview
- Introduction
- GEE background
- GEE applications
- GEE Pre-requirements
Basics of Google Earth Engine oriented JavaScript (JS) Programming language
- Basic programming concepts
- Mostly used GEE JS API syntax
- How to write efficient code guide
Working with vector and raster datasets
- Where to get different datasets for GEE
- How to import and visualize vector datasets
- How to import and visualize raster datasets
- How to import your own vector or raster dataset in GEE
Filtering the feature or image collection
- The need for filtering datasets
- Different types of filters
- When and where to use filters
- Reducing and Clipping image collection
The role of reducers
- Different types of reducers
- Converting image collections into single image
- Operators
- Use of operators in programming
- Efficient use of Operators in GEE
- Evaluating NDVI using operators
Automating the analysis in GEE
- The need of automation in GEE
- The concept of For-Loops (with reference to Python)
- Implementation of For-Loops in GEE using “.Map” function.
Goals
- Foundations and applications of Google Earth Engine (GEE)
- JavaScript basics for efficient GEE programming
- Importing, visualizing, and handling vector and raster data sets
- Filtering, aggregating, and clipping geospatial data sets
- Applying operators and reducers for data processing
- Automating geospatial workflows using.map() and For-Loops
- Practices for efficient and optimized GEE coding
- Foundations and applications of Google Earth Engine (GEE)
- JavaScript basics for efficient GEE programming
- Importing, visualizing, and handling vector and raster data sets
- Filtering, aggregating, and clipping geospatial data sets
- Applying operators and reducers for data analysis
- Geospatial workflow automation with .map() and For-Loops
- Writing optimized and efficient GEE code: best practices.
Prerequisites
- Basic understanding of geospatial concepts (optional but helpful)
- Familiarity with satellite imagery and remote sensing (recommended)
- No prior programming experience required, but basic coding knowledge is a plus
- A Google account to access Google Earth Engine
- Interest in environmental analysis, remote sensing, or GIS applications
- Stable internet connection for working with cloud-based datasets
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Google Earth Engine Course overview
08:19
08:19
Basics of Google Earth Engine oriented JavaScript (JS) Programming language
1 Lectures
Working with vector and raster datasets - Google Earth Engine
2 Lectures
The role of reducers - Google Eath Engine
2 Lectures
Automating the analysis in GEE
1 Lectures
Instructor Details
Golgi Alvarez
Researcher, writer and rich content creator
More than 25 years working int the Geospatial and AEC industries:
- Infrastructure and civil work projects management
- CAD / BIM / GIS / Surveying & Construction training
- Land and Cadastre projects
- Land Administration and development projects.
Golgi Alvarez is the founder of the Geofumadas website, with more than 1.900 articles about CAD, GIS, Surveying, Cadastre, BIM, Digital Twin and other trend topics. The website is over 170.000 visitors per month and 21.000 followers in twitter.Golgi Alvarez is the founder of the TwinGEO magazine, attending on site conferences in USA, UK, Netherlands, Singapore, India, Spain, Mexico, Colombia, Chile, Guatemala and other countries.
Golgi Alvarez is the founder of the AulaGEO Academy, with more than 150 courses focused in geospatial, engineering and operation of land and infrastructure projects.
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