Lab 8. Scaling Geospatial Machine Learning: Exploring the Spatial Transferability of RF Models скачать в хорошем качестве

Lab 8. Scaling Geospatial Machine Learning: Exploring the Spatial Transferability of RF Models

Summary In this tutorial, we explore how to scale geospatial machine learning by testing the spatial transferability of a Random Forest model trained in Bulawayo, Zimbabwe, on satellite imagery from Gweru. You'll learn how to apply a pre-trained model using Sentinel-2 and ALOS PALSAR HV data, generate a new land cover map, and evaluate whether the model generalizes across space. 🔍 What you will learn: How to apply a Random Forest model to a new region What spatial transferability means in geospatial ML The limitations and challenges of transferring models How to generate and export a land cover classification map 📦 Tools Used: Google Colab • Python • scikit-learn • rasterio • joblib • Sentinel-2 • ALOS PALSAR Watch now to make your land cover models more scalable and reusable! Additional Materials: 1. Python Script https://github.com/ck1972/Geospatial-... 2. Access courses at Ai. Geelabs https://aigeolabs.com/courses/ https://aigeolabs.com/sign-up/ 3. Buy 'Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach' book https://aigeolabs.com/books/explainab...