Google Earth Engine for Machine Learning & Change Detection

Become Expert in Spatial analysis & Remote Sensing for machine learning in land use / land cover in Google Earth Engine

What you'll 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

Requirements
A working computer with internet connection
An interest in working with geospatial data

Description
Land Use/Land Cover Mapping and Change Detection with Machine Learning in Google Earth EngineAre you ready to elevate your geospatial analysis skills and become proficient in land use and land cover (LULC) mapping and change detection? This comprehensive course is designed to empower users who have a basic background in GIS, geospatial data, and remote sensing with the knowledge and tools required for advanced geospatial analysis.Course Highlights:Extensive coverage of machine learning algorithms and their practical applicationIn-depth understanding of Google Earth Engine for LULC mapping and change detectionStep-by-step guidance on acquiring satellite data, preprocessing, spectral indices calculation, and change map designReal-world projects and practical exercises to reinforce your skillsDownloadable materials, including data and Java code filesAccess to future resources to support your geospatial endeavorsCourse Focus:This course is more than just theory; it's about hands-on learning and practical implementation. You'll gain proficiency in unsupervised and supervised classification strategies for LULC mapping, which is a fundamental skill for GIS and remote sensing analysts. By the end of this course, you'll feel confident in performing advanced geospatial analysis, including machine learning algorithms for mapping and change detection, all using real and openly available data in Google Earth Engine.Why Choose This Course:Unlike other training resources, every lecture in this course aims to enhance your GIS and remote sensing skills in a clear and actionable manner. You'll be equipped to analyze spatial data for your own projects and earn recognition from future employers for your advanced GIS skills and knowledge of cutting-edge LULC techniques.What You'll Learn:Google Earth Engine sign-in and interface navigationData preprocessing on the cloud and spectral indices calculationIntroduction to JavaScriptMachine learning theory and its application in GISClassification of satellite images using various machine learning algorithms (supervised and unsupervised) in Google Earth EngineTraining, validation data collection, and accuracy assessmentChange detection techniques in Google Earth EngineCompletion of your own geospatial project on the cloudEnroll Today:If you're a geographer, programmer, social scientist, geologist, or any professional seeking to use LULC maps in your field and want to master state-of-the-art classification algorithms for tasks like land cover and land use mapping, this course is your solution. Sign up now and unlock the confidence and expertise to tackle complex geospatial challenges!INCLUDED IN THE COURSE: You'll have access to all the data used throughout the course, along with Java code files. Plus, you'll enjoy access to future resources, making this course a valuable investment in your geospatial career. Enroll today and take advantage of these special materials!

Who this course is for:
Geographers, Programmers, geologists, biologists, social scientists, or every other expert who deals with GIS maps in their field