Lab 6b. Random Forest Regression Modeling for AGBD - AI.GEOLABS

Return to course: Geospatial Machine Learning (ML) in the Age of AI AI.GEOLABS

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Geospatial Machine Learning (ML) in the Age of AI

Lab 1: Introduction & AI-Assisted Orientation

Lab 2: Python & Spatial Data Fundamentals

Lab 3: Machine Learning Fundamentals for Spatial Data

Lab 4a: Preparing Training Samples for Land Cover Classification
Lab 4b: Preparing Training Samples for Supervised Land Cover Mapping: Optical and Synthetic Aperture Radar (SAR) Data

Lab 5a: Land Cover Classification with Machine Learning
Lab 5b: Improving Land Cover Classification Using Optical and Synthetic Aperture Radar (SAR) Imagery

Lab 6a. Preparing Training Data for AGBD Modeling
Lab 6b. Random Forest Regression Modeling for AGBD
Lab 6c. Improving AGBD Training Data
Lab 6d. Optimising Random Forest Regression with Spatial Validation
Lab 6e. Explainable ML for AGBD Regression (SHAP)

Lab 7a. Explainable ML - SHAP for Land Cover Classification
Lab 7b. Explainable ML - SHAP for AGBD Regression

Lab 8. Scaling Geospatial ML - Spatial Transferability

Lab 9. Unsupervised ML - Clustering & Anomaly Detection

Lab 10. Final Capstone Project

Lab 6b. Random Forest Regression Modeling for AGBD

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