If you are interested in mastering Land Use and Land Cover (LULC) prediction maps using Machine Learning on Google Earth Engine, consider joining our upcoming private online training program. This comprehensive 5-day course will equip you with the skills to apply machine learning techniques to create accurate LULC maps, perform change detection, and predict future land cover scenarios.

Course Syllabus:

1. Introduction to Machine Learning in Google Earth Engine
2. Creating LULC Maps using Supervised Machine Learning
3. LULC Mapping with Unsupervised Algorithms
4. Implementing Random Forest Algorithm
5. Applying CART Algorithm
6. Utilizing Support Vector Machine Algorithm
7. Using Minimum Distance Algorithm
8. Clustering Algorithms
9. Accuracy Assessment of LULC Maps (Kappa, Producer’s, and Consumer’s Accuracy)
10. Calculating LULC Class Areas
11. Adding Legends to LULC Maps
12. Change Detection Mapping using Google Earth Engine
13. Predicting Future LULC with Machine Learning
14. CA-ANN using QGIS
15. Creating Predictive Factors
16. Enhancing LULC Accuracy
17. Hyperparameter Tuning for Improved Model Accuracy
18. Exporting LULC Data and Preparing Research Papers with ArcMap

Prerequisites:

• Advanced knowledge of Google Earth Engine
• Basic understanding of Remote Sensing

Training Benefits:

• E-Certificate upon completion of assignments
• Course materials (Slides, PDFs)
• Practice codes provided
• Access to recorded class videos
• Lifetime teaching support