Lab 5a: Land Cover Classification Using Machine Learning: An Introductory Guide with Scikit-Learn скачать в хорошем качестве

Lab 5a: Land Cover Classification Using Machine Learning: An Introductory Guide with Scikit-Learn

Summary In this tutorial, you will be introduced to the fundamentals of applying machine learning algorithms for land cover classification using satellite imagery. Land cover classification is a key task in remote sensing that supports a wide range of applications, including environmental monitoring, urban planning, agriculture, and natural resource management. This lab walks you through how to use four popular classifiers—K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forests—implemented in Python using the Scikit-Learn library. You will learn how to load training and testing data, preprocess features, train different models, evaluate their performance using accuracy metrics, and visualize the classification results. Scikit-Learn is chosen for its user-friendly API and robust implementation of various machine learning algorithms, while Google Colab offers a convenient environment with pre-installed libraries and integration with Google Drive. By the end of this lab, you will have a solid foundation in applying and assessing basic machine learning models for classifying land cover types from Earth observation data. This tutorial is ideal for students, early-career professionals, and anyone interested in geospatial analysis and machine learning. Be sure to like the video, subscribe to the channel, and hit the notification bell to stay updated on future tutorials. 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...