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Machine Learning in GIS : Understand the Theory and Practice

Name: Machine Learning in GIS : Understand the Theory and Practice
Rating: 4.0 (361 reviews)

Apply machine learning in Geographic information systems (GIS) and Remote Sensing in QGIS and Google Earth Engine (GEE)

Created byKatie Alison

Last updated 11/2024

English

What you'll learn

Fully understand the basics of Machine Learning
Get an introduction to Geographic Information Systems (GIS), geodata types and GIS applications
Fully understand basics of Remote Sensing
Learn open source GIS and Remote Sensing software tools (QGIS, Google Earth Engine and others)
Fully understand the main types of Machine Learning and their applications in GIS
Learn about supervise and unsupervise learning and their applications in GIS
Learn how to apply supervised and unsupervised Machine Learning algorithms in QGIS and Google Earth Engine
Understand what is segmentation, object-based image analysis (OBIA) and predictive modeling in GIS
Learn how to perform image segmentation with Orfeo Toolbox
Understand the main developments in the field of Artificial Intelligence, deep learning and machine learning as applied to GIS

Course content

9 sections • 38 lectures • 6h 20m total length

Introduction3:17
In this lecture, you will learn the main objectives of the course, its goals, the coarse structure and what topics are going to be covered in the course on Machine Learning in Geographic Information Systems (GIS) and Remote Sensing.
GIS explained5:37
Explore the world of spatial analysis and cartography with geographic information systems (GIS). In this class, you will learn the basics of GIS, it's definition, applications and main data types.
Introduction to Remote Sensing: definition5:12
In this class, you will learn a definition of Remote Sensing.
Introduction to Remote Sensing: applications9:09
Explore the world of spatial analysis with Earth Observation (or Remote Sensing), In this class, you will learn the variety of the applications of Remote Sensing.

Computer Set up for GIS analysis and GIS software on the market11:59
In this class, you will learn how to how to set up a GIS on your computer, specifically what you will need to run GIS analysis on your personal computer. We will also talk about software tools available for the GIS analysis. In the practical section of the course, you will learn how to correctly install and set up QGIS on your computer to get ready for GIS analysis.
QGIS version information2:21
Installing QGIS12:39
In this video, I will show you how to install QGIS on your computer.
A note on QGIS versions and it's plug-ins8:44
Exploring QGIS interface9:46
In this video, we will explore together the QGIS interface.
A power of QGIS - QGIS Plug-ins7:45
In this video, I will show you how to install and manage QGIS plug-ins.
Lab: Sign In to Google Earth Engine3:37

Introduction to Machine Learning16:03
During this lecture, I’m going to explain what ML is, the types of machine learning algorithms and when you should use each of them.
On Machine Learning in GIS and Remote Sensing8:19
During this video lecture, I’m going to explain the application of machine learning (ML) algorithms in GIS and Remote Sensing, types of ML applications in GIS and I will provide you with some practical examples.
OTB installation2:12
In this video, you will learn how to install the OTB toolbox in your QGIS software. A detailed description of the procedure is provided in the resource section for this video.

Supervised and Unsupervised Learning (classification) in GIS and Remote Sensing9:27
During this lecture, we are going to learn about image classification and ist types. Here we will talk about the supervised and unsupervised learning in the context of GIS and I also provide you with workable examples.
Lab: Unsupervised Image Classification in SCP9:46
Land cover classification on the cloud using EO browser11:29
Unsupervised (K-means) image analysis in QGIS4:52
Random Forest supervised classification of Sentinel-2 image25:20
In this video, you will learn to classify an image with a random forest algorithm through the Orfeo Toolbox (OTB). The data needed to complete this practical as well as the detailed guidance is provided in the resource section of this video.
Decision Trees classification of Sentinel-2 image17:53
In this video, you will learn to classify an image with a Decision Tree algorithm through the Orfeo Toolbox (OTB). Detailed guidance is provided in the resource section of this video. The data needed to complete this practical was provided in Lecture 13 of this course.
Accuracy Assessment11:13
IN this video lecture, you will learn how o perform accuracy assessment for a case of supervised classification.
Support Vector Machine (SVM) supervised classification of the satellite imagery

EO browser for image download, spectral indices & land cover19:15
Supervised classification with Google Earth Engine18:57
In this video, you will learn to perform image classification in Google Earth Engine cloud-computing platform.
Import images and their visualization in Google Earth Engine16:11
Unsupervised (K-means) image analysis in Google Earth Engine8:34
Random Forest Classification in Earth Engine17:34
Accuracy Assessment in Google Earth Engine5:53

Object detection in GIS5:31
During this video lecture, we are going to continue exploring types of machine learning and today we are going to talk about object detection in GIS. I will provide you with an overview of how it works and I will demonstrate this with the practical examples.
Segmentation and object-based image analysis (OBIA)4:42
In this video, I will introduce you to the term segmentation and object-based image analysis and explain to you the advantage of this approach as opposed to more traditional pixel-based image analysis.
Segmentation of high-resolution satellite image10:26
In this video, you will learn to perform image segmentation in QGIS with the OTB toolbox following my instructions. The Sentinel 2 image needed to complete this practical was provided in Lecture 13 of this course.

On regression in GIS3:29
ArcGIS Software for regression analysis2:59
Lab: Use regression analysis in ArcGIS25:04
Prediction in GIS and deep learning for Big Data Analysis7:08
Prediction is an important part of GIS applications that use Machine Learning and AI. In this video lecture, I will introduce you to the notion of prediction modeling in GIS and equip you with the main types of prediction models used in GIS. Finally, we are going to talk about the new developments in AI and Machine Learning in GIS and Remote Sensing including deep learning for Big Data analysis.

Requirements

A working computer

Description

Are you ready to apply Machine Learning to real geospatial problems but unsure how to begin? This course provides a clear, practical introduction to Machine Learning for Geographic Information Systems (GIS) and Remote Sensing. You will learn both the theoretical foundations and the hands-on workflows needed to use Machine Learning for land use and land cover mapping, image classification, segmentation, and other essential geospatial tasks.

Designed for learners who want practical skills rather than abstract theory, this course demonstrates how to run Machine Learning algorithms in QGIS, how to perform segmentation and object-based analysis, and how to use Google Earth Engine for cloud-based mapping with satellite images.

Course Highlights

• Theoretical and practical knowledge of Machine Learning in GIS and Remote Sensing
• Hands-on application of Machine Learning algorithms such as Random Forest, SVM, and Decision Trees
• Execution of a complete GIS project with real data
• Cloud-based geospatial processing using Google Earth Engine
• Clear, step-by-step demonstrations with downloadable materials
• Practical examples suitable for academic, research, and professional use

Course Focus

This course integrates Machine Learning theory with real geospatial workflows. You will learn how to preprocess satellite images, classify them using modern algorithms, run segmentation workflows, and create land cover maps in QGIS and Google Earth Engine. By the end of the course, you will have the skills and confidence to apply Machine Learning to a wide range of Remote Sensing and GIS applications.

What You Will Learn

• Installing and configuring open-source GIS tools such as QGIS and the OTB toolbox
• Navigating the QGIS interface, plug-ins, and processing tools
• Classifying satellite images using Random Forest, SVM, Decision Trees, and other algorithms
• Performing image segmentation and object-based analysis in QGIS
• Creating land cover maps using Google Earth Engine
• Preparing training and validation samples for Machine Learning
• Understanding key Machine Learning concepts relevant to spatial data

Who Should Enroll

This course is ideal for:
• Geographers and environmental scientists
• GIS and Remote Sensing analysts
• Programmers and data scientists entering geospatial work
• Social scientists and geologists using spatial data
• Anyone who needs to apply Machine Learning for LULC mapping or geospatial tasks

If you expect to use modern Machine Learning algorithms for spatial classification, object-based analysis, or land cover mapping, this course will give you the tools and confidence to succeed.

Included in the Course

• Step-by-step instructions
• Datasets and QGIS project files
• Downloadable code for Google Earth Engine
• Practical exercises for each major workflow

Enroll today and unlock the full potential of Machine Learning for geospatial 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 : Understand the Theory and Practice

What you'll learn

Explore related topics

Course content

Introduction to the course, GIS and Remote Sensing4 lectures • 23min

Installation of QGIS on your Computer7 lectures • 57min

Introduction to Machine Learning in GIS3 lectures • 27min

Types of supervised & unsupervised machine learning and applications in GIS7 lectures • 1hr 30min

New: Image classification in QGIS: how to create training and run classification2 lectures • 28min

Machine Learning in Google Earth Engine6 lectures • 1hr 26min

Introduction to object-based machine learning in GIS and QGIS3 lectures • 21min

Predictions and regression in GIS and deep learning for Big Data Analysis4 lectures • 39min

Final Project: Machine Learning for GIS on cloud (Google Earth Engine)2 lectures • 8min

Requirements

Description

Who this course is for: