Google Earth Engine for Machine Learning & Change Detection
What you will learn
Students will gain access to and a thorough knowledge of the Google Earth Engine platform
Implement machine learning algorithms on geospatial (satellite images) data in Earth Engine for LULC mapping
Get introduced and advance JavaScript skills on Google Earth Engine platform
Fully understand the main types of Machine Learning (supervised and unsupervised learning)
Learn how to apply supervised and unsupervised Machine Learning algorithms in Google Earth Engine
Learn how to obtain satellite data, apply image preprocessing, create training and validation data in Google Earth Engine
Implement calculation of change detection (pre and post-event detection) based on spectral indices
You'll have a copy of the codes used in the course for your reference
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Comidoc Review
Our Verdict
The "Google Earth Engine for Machine Learning & Change Detection" course strives to deliver an in-depth look at geospatial analysis and remote sensing. While it may suffer from some structural issues and occasional discrepancies, its strong points still make it an effective learning opportunity. The course excels at educating students about the Google Earth Engine platform, improving JavaScript skills, and providing hands-on projects to put theory into practice. However, be prepared for minor inconsistencies due to outdated materials or inadequate presentation of certain topics. Furthermore, some users have expressed disappointment about the lack of machine learning focus when compared to the course's title. Keep this factor in mind before diving in or while setting your expectations accordingly.
What We Liked
- Comprehensive coverage of Google Earth Engine, including data acquisition, preprocessing, and machine learning algorithms
- Provides a solid foundation in JavaScript for Google Earth Engine platform
- Hands-on experience with change detection and land use/land cover mapping techniques
- Access to well-explained code examples used throughout the course
Potential Drawbacks
- Concerns about course organization, overlapping content, and repetitive material
- Potential for outdated information and incomplete updates in line with Google Earth Engine's changes
- Occasional issues with the presentation of algorithms, making it difficult to grasp certain concepts without external research
- Limited focus on true machine learning applications compared to the expectation set by the course title