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Geospatial Data Analysis and Remote Sensing: 5 Courses in 1

Name: Geospatial Data Analysis and Remote Sensing: 5 Courses in 1
Rating: 4.4 (393 reviews)

Remote Sensing, QGIS, GIS fundamentals, machine learning, QGIS classification, change detection, and Earth Engine

Created byKatie Alison

Last updated 11/2025

English

English [Auto],

What you'll learn

Understand and implement basic concepts of Geographic Information Systems (GIS) and Remote Sensing
Fully understand the basics of Land use and Land Cover (LULC) Mapping and Change Detection in QGIS
Learn the most popular open-source GIS and Remote Sensing software tools (QGIS), Semi-automated classification (SCP) plugin, OTB toolbox)
Learn how to obtain satellite data, apply image preprocessing, create training and validation data in QGIS
Create your first GIS maps for your reports/presentations in QGIS
Understand machine learning concepts and its application in GIS and Remote Sensing
Apply Machine Learning image classification mapping and change detection in SCP, OTB toolbox and QGIS
Fully understand and apply advanced methods in machine learning in GIS and Remote Sensing, such as random forest classification and object-based image analysis,
You'll have a copy of the labs, step-by-step manuals and scripts used in the course for QGIS & more

Course content

12 sections • 64 lectures • 8h 56m total length

Introduction to the course4:22
Explore the fundamentals of GIS and remote sensing, including land use mapping, change detection, and machine learning with open-source tools like QGIS and Google Earth Engine.
Applications of GIS6:51
Definition of GIS1:26
Applications of Remote Sensing7:41
Definition of Remote Sensing5:12

About open-source QGIS software5:47
QGIS version information2:21
A note on QGIS versions and it's plug-ins8:44
Learn how to select and install stable QGis versions and older releases. Manage plugins like semi automatic classification plugin and KSP plugin, and ensure compatibility for remote sensing analyses.
Lab: QGIS installation12:39
Semi-Automatic Classification Plugin for QGIS2:56
Lab: QGIS interface7:45
Lab: QGIS Toolbars9:46
OTB installation2:12
Lab: Creating account in Google Earth Engine3:37

Sensors and Platforms5:01
Introduction to digital images11:21
Examine digital images, pixels, and pixel values, then differentiate spatial, spectral, radiometric, and temporal resolutions in multispectral and hyperspectral data.
Lab: How to download satellite images with SCP plug-in10:20
Learn to download satellite imagery for study areas using the semiautomatic classification plugin in QGIS, including setting up accounts and selecting coordinates, dates, and cloud-free Landsat products.

Lab: Layerstacking, True and False Colour composites15:15
Learn to build a multilayer Landsat 8 stack in Kuji and create true color and false color composites to visualize vegetation in the Amazon rainforest.
Lab: Image preprocessing - atmospheric correction7:50
Sources of Remote Sensing images11:20
Access a range of remote sensing data sources for imagery download and analysis, including Google Earth Engine, Landsat Explorer, Sentinel Hub, Earth Explorer, and Copernicus Open Access Hub.
EO browser: Using cloud platform for spectral indices & land cover analysis19:15

Introduction: Machine Learning9:27
Explore how machine learning uses data-driven algorithms to learn from data. Identify the main types: supervised, semi supervised, unsupervised, and reinforcement learning, and how labeling shapes predictions.
Understanding Remote Sensing for LULC mapping6:59
Introduction to LULC classification based on satellite images3:09
Supervised and unsupervised image classification9:31
Examine supervised and unsupervised image classification for land use, using training data to label spectral attributes or uncover natural spectral clusters with clustering algorithms such as k-means.
Unsupervised (K-means) image analysis in QGIS4:52
Stages of LULC supervised classification12:18
define mutually exclusive and exhaustive land use and land cover classes, collect representative training data, and classify pixels to produce a land use map, followed by accuracy assessment.
Overview of image classification algorrithms19:18
Explore a range of supervised and unsupervised image classification algorithms, including minimum distance to mean, maximum likelihood, decision trees, random forest, and support vector machines, with examples and accuracy considerations.
Accuracy assessment of LULC map11:13

Lab: Training data collection in QGIS24:06
Lab: LULC with the use of spectral angle mapping9:17
Lab: LULC with the use of Maximum Likelihood Algorithm5:28
Lab: LULC with the use of Minimum Distance Classification Algorithm3:51
Lab: Validation data creation12:44
Lab: Accuracy Assessment13:37
Project: LULC mapping of Landsat 81:46
Map land use land cover from a 2013 Landsat 8 image of Brazil using dark-object subtraction atmospheric correction, prepare training and validation data, classify with three algorithms, and assess accuracy.

Section Overview
Supervised classification with Google Earth Engine (explorer)18:57
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 Supervised CLassification in Earth Engine17:34
Learn to perform supervised classification in Google Earth Engine with sentinel imagery and a random forest classifier, creating training data for water, vegetation, burn, sand, and rocks.
Accuracy Assessment in Earth Engine5:53

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.
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.
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.
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.
Project: Machine Learning for GIS on cloud (Google Earth Engine)6:14

Requirements

A working computer
An interest in working with spatial data
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

Geospatial Analysis and Remote Sensing: From Beginner to Pro

Are you struggling to create GIS maps or work with satellite imagery for your Remote Sensing projects? Do concepts like object-based image analysis, machine learning, QGIS, or Google Earth Engine seem overwhelming? This course provides a clear and practical pathway to understanding and applying these methods through real geospatial projects.

This 5-in-1 Practical Geospatial Masterclass combines the content of multiple courses into one comprehensive learning experience. With over nine hours of video lessons, hands-on exercises, and downloadable resources, you will build strong skills in geospatial analysis, Remote Sensing, machine learning, and GIS data processing using popular and free software tools.

Course Highlights

• Comprehensive theoretical and applied geospatial knowledge
• Machine learning applications for GIS and Remote Sensing tasks
• Land use and land cover mapping workflows
• Object-based image analysis and segmentation
• Data preprocessing, manipulation, and map creation
• Hands-on practice in QGIS and Google Earth Engine
• Real project workflows using open-source tools

Course Focus

This masterclass will guide you through Remote Sensing, GIS, and Machine Learning applications step by step. You will learn how to download and preprocess satellite imagery, perform supervised and unsupervised learning, compute accuracy assessment, run change detection, and apply geospatial algorithms for real environmental and land-use analysis.

By the end of the course, you will understand key concepts in Remote Sensing, GIS fundamentals, machine learning for geospatial tasks, and object-based image analysis. You will also be able to conduct practical geospatial workflows using QGIS, Google Earth Engine, and other open-source tools.

What You Will Learn

• Applying machine learning algorithms in QGIS for LULC analysis
• Downloading, preprocessing, and analyzing satellite images
• Supervised and unsupervised classification techniques
• Accuracy assessment and change detection workflows
• Object-based image analysis and segmentation
• Cloud computing and Big Data geospatial analysis with Google Earth Engine
• Creating maps and final outputs for GIS projects

Who Should Enroll

This course is ideal for geographers, programmers, environmental scientists, social scientists, geologists, GIS and Remote Sensing specialists, and anyone wanting to improve their geospatial and machine learning skills. Whether you are a beginner or looking to advance your expertise, this course gives you the confidence and knowledge to tackle real geospatial challenges.

Included in the Course

You will receive access to datasets, scripts, step-by-step instructions, and practical materials for all exercises in QGIS and Google Earth Engine. Enroll today and unlock the full potential of practical geospatial analysis.

Who this course is for:

Geographers, Programmers, geologists, biologists, social scientists, or everyone who deals with GIS maps in their field or would like to learn GIS and Remote Sensing

Geospatial Data Analysis and Remote Sensing: 5 Courses in 1

What you'll learn

Explore related topics

Course content

Introduction to the course, GIS and Remote Sensing5 lectures • 26min

Software used in this course9 lectures • 56min

Basics of GIS4 lectures • 31min

Introduction to Remote Sensing (theory)3 lectures • 27min

Practicals: basics of satellite image analysis (Remote Sensing) in QGIS4 lectures • 54min

Basics of land use and land cover (LULC) mapping (theory)8 lectures • 1hr 17min

Practicals: Image CLassification in Semi-Automated Classification Plugin in QGIS7 lectures • 1hr 11min

Introduction to change detection in QGIS3 lectures • 29min

Image Classification in Google Earth Engine6 lectures • 1hr 7min

Introduction to Machine Learning in GIS7 lectures • 57min

Requirements

Description

Who this course is for: