Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Machine Learning in GIS: Land Use and Land Cover analysis

Name: Machine Learning in GIS: Land Use and Land Cover analysis
Rating: 4.3 (211 reviews)

Advance Remote Sensing and GIS pixel-based and object-based image analysis in Google Earth Engine and QGIS

Created byKatie Alison

Last updated 11/2025

English

What you'll learn

Fully understand advanced methods of Land use and Land Cover (LULC) Mapping in QGIS and Google Earth Engine
Learn how to perform such advanced methods as object based image analysis (OBIA) and object-based classification using real-world data in QGIS
Further advanced your skills in the most popular open-source GIS and Remote Sensing software tools (QGIS)
Learn how to obtain satellite data, apply image pre-processing, create training and validation data for OBIA in QGIS and Google Earth Engine
Apply advanced Machine Learning image classification algorithms
Create and download LULC maps for your report
Explore the power of Google Earth Engine for image analysis
You'll also have plenty of handy hints and tips will be provided alongside the code to prevent glitches
You'll have a copy of the labs’ step-by-step manuals used in the course for your reference to use in your analysis.

Course content

8 sections • 36 lectures • 4h 38m total length

Introduction3:37

Overview: Software used in this course
QGIS version information2:21
A note on QGIS versions and it's plug-ins8:44
Lab: Installing QGIS and install SCP12:39
Lab: Sign In to Google Earth Engine3:37
Sign up for Google Earth Engine to access a cloud platform for remote sensing data and analysis, with a JavaScript code editor and Landsat archives for land use classification.
Orfeo Toolbox installation2:12
Install Orfeo Toolbox for image classification in GIS by following a step-by-step setup guide, then activate the toolbox in your GIS software and access the OTB panel.

Section Overview
Introduction to Machine Learning16:03
Basics of machine learning for classification analysis9:27
Unsupervised (K-means) image analysis in QGIS4:52
Common algorithms of image classification19:18
Land cover classification on the cloud using EO browser11:29
Random Forest classification in OTB25:20
learn to perform image classification with a random forest model in a GIS workflow, using training and validation data on sentinel-2 imagery to classify water, vegetation, and built-up areas.
SVM classification in OTB1:51
Practice image classification using support vector machines in OTB, guided by random forest and decision trees, and prepare training and validation data to classify a separate image.
DT classification in OTB17:53
Accuracy Assessment11:13

Section Overview
Import images and their visualization in Google Earth Engine16:11
Import and visualize Sentinel-2 data in Google Earth Engine for Dubai. Create a workflow to display true color and false color composites and build land cover maps with supervised methods.
Unsupervised (K-means) image analysis in Google Earth Engine8:34
Random Forest Supervised CLassification in Earth Engine17:34
Accuracy Assessment in Earth Engine5:53
Test accuracy of land use land cover classifications in Google Earth Engine with a Dubai example. Build a validation dataset, compute confusion metrics, and report overall accuracy.

Section Overview
Introduction to object-based image classification in QGIS8:45
Explore object-based image classification in QGIS with Orfeo Toolbox, covering segmentation, feature extraction, and training and validation data to generate crop maps from Sentinel composites.
Feature Extraction for object-based crop classification5:34
Training of Machine learning Classifiers for object-based crop classification9:52
Object-based crop classification with Machine Learning algorithms in QGIS10:08

Section Overview
Object-based image classification (OBIA) VS pixel-based image classification6:09
Segmentation of high-resolution satellite image7:36
Creating training data from satellite image based on the segmented layer8:09
Create training data for land use classification by labeling segmented polygons as water, vegetation, or built-up, then export training and validation datasets for offline model training.
Object-based image classification with the Machine Learning algorithm11:00

Requirements

A vivid interest in working with geospatial data
Basic knowledge of manipulating spatial (image) data using QGIS
A working computer with internet connection
The course will be demonstrated using a QGIS version of Windows PC. Mac and Linux users will have to adapt the instructions to their operating systems.

Description

Advanced Land Use and Land Cover Mapping with Machine Learning

Are you ready to take your geospatial analysis skills to the next level using QGIS and Google Earth Engine? Do you want to master object-based image analysis and apply powerful Machine Learning algorithms for Land Use and Land Cover (LULC) mapping? This course is designed for learners with basic GIS experience who want to perform advanced geospatial tasks with confidence.

You will explore pixel-based and object-based image analysis, work with multiple data sources, and apply advanced Machine Learning techniques for LULC classification, change detection, and object-based crop mapping. All workflows are demonstrated in QGIS and Google Earth Engine using real satellite data.

Course Highlights

• Advanced geospatial analysis and Remote Sensing in QGIS
• Object-based image analysis (OBIA) workflows
• Machine Learning algorithms for LULC mapping
• Practical exercises using QGIS and Google Earth Engine
• Installation and configuration of open-source GIS software
• Supervised and unsupervised Machine Learning techniques
• Accuracy assessment for geospatial classification projects

Course Focus

This course provides a practical introduction to advanced LULC mapping and object-based image analysis. You will gain confidence using Machine Learning algorithms for environmental and spatial analysis tasks, while leveraging the capabilities of QGIS and Google Earth Engine. By course completion, you will understand how to classify satellite images, design geospatial workflows, and evaluate outputs accurately.

What You Will Learn

• Installing and configuring QGIS and the Orfeo Toolbox
• Navigating the QGIS interface and essential plug-ins for Remote Sensing
• Classifying satellite imagery using Machine Learning algorithms in QGIS
• Collecting training and validation data and performing accuracy assessments
• Performing object-based image analysis and object-based crop type mapping
• Running supervised and unsupervised Machine Learning algorithms in Google Earth Engine
• Building LULC classification workflows from start to finish

Who Should Enroll

This course is ideal for geographers, GIS and Remote Sensing specialists, programmers, social scientists, geologists, environmental analysts, and anyone who needs to create land cover and land use maps. If you want to tackle advanced geospatial challenges or use cutting-edge LULC techniques, this course will give you the skills and confidence you need.

Included in the Course

You will gain access to all datasets, JavaScript code files, and additional materials used throughout the course, as well as future updates and resources.

Enroll today and take your geospatial and Remote Sensing skills to the next level with advanced Machine Learning and LULC analysis in QGIS and Google Earth Engine.

Who this course is for:

Geographers, Programmers, geologists, biologists, social scientists, or every other expert who deals with GIS maps in their field

Machine Learning in GIS: Land Use and Land Cover analysis

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 4min

Software used in this course6 lectures • 30min

Advanced LULC methods with open spurce software and tools10 lectures • 1hr 57min

Image Classification in Google Earth Engine5 lectures • 48min

Object-based crop classification with Machine Learning algorithms in QGIS5 lectures • 34min

Object-based image classification with training data creation5 lectures • 33min

LULC map visualization in QGIS2 lectures • 10min

Final Project2 lectures • 2min

Requirements

Description

Who this course is for: