Land Cover Classification In Google Earth Engine

Start applying machine learning for remote sensing

What you'll learn

Get Theoretical Knowledge of Random Forest Algorithm

Proficiency in Google Earth Engine

Training Data Development

Land Cover Mapping

Accuracy Assessment

Requirements

A free Google Earth Engine account (enrollment instructions provided)

Access to a computer with a reliable internet connection

Description

Welcome to an in-depth and rigorously structured course designed to equip learners with the expertise to perform land cover classification using Random Forest within Google Earth Engine (GEE). This course is tailored for students, geospatial professionals, environmental scientists, and researchers seeking to harness satellite imagery for precise land cover mapping. Through a comprehensive case study in Çumra District, Konya, Türkiye, participants will develop proficiency in classifying land into four categories—Water, Vegetation, Urban, and Bare Land—utilizing state-of-the-art machine learning techniques and cloud-based geospatial platforms. No prior experience in coding or remote sensing is required, as this course provides a systematic progression from foundational concepts to advanced applications, ensuring accessibility for beginners and value for experienced learners.Upon completion, you will produce a professional-grade land cover map of Çumra District, demonstrating mastery of Random Forest and GEE. You will gain the ability to preprocess satellite imagery, develop and validate machine learning models, and interpret geospatial data, skills highly valued in academia and industries such as environmental management, urban planning, and agricultural monitoring.Embark on a transformative learning journey to master land cover classification with Random Forest in Google Earth Engine. This course offers a unique opportunity to develop cutting-edge skills through a practical, real-world project in Çumra District, equipping you to address global environmental challenges. Enroll now to gain expertise in geospatial analysis, contribute to sustainable development. Begin your journey today and unlock the potential of satellite imagery to map and understand our world.

Overview

Section 1: Introduction

Lecture 1 What is Google Earth Engine?

Lecture 2 Google Earth Engine Interface

Lecture 3 Landsat 8 Image Visualization

Section 2: Random Forest Algorithm

Lecture 4 What is Random Forest?

Lecture 5 Overfitting

Section 3: Land Cover Classification

Lecture 6 Create Training Dataset

Lecture 7 Combine Training Points

Lecture 8 Choose Features

Lecture 9 Get Feature Values

Lecture 10 Split Data for Training and Testing

Lecture 11 Train Random Forest

Lecture 12 Classify the Image

Lecture 13 Land Cover Mapping

Lecture 14 Potential Questions and Answers

Section 4: Accuracy Assesment

Lecture 15 Intro

Lecture 16 Classify the Validation Set

Lecture 17 Create a Confusion Matrix

Lecture 18 Potential Questions and Answers

Undergraduate and graduate students in environmental science, geography, or related fields seeking practical geospatial skills,Geospatial professionals aiming to integrate machine learning and GEE into their workflows.,Researchers and analysts interested in leveraging satellite imagery for environmental and urban studies