Geospatial Analyses & Remote Sensing : from Beginner to Pro

Learn Remote Sensing, QGIS & GIS , main concepts, machine learning, QGIS classification, change detection, Earth Engine

4.61 (71 reviews)



8.5 hours


Feb 2021

Last Update
Regular Price

What you will 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, in QGIS and Google Earth Engine

You'll have a copy of the labs, step-by-step manuals and scripts used in the course for QGIS & more




Geospatial Data Analyses & Remote Sensing: 5 Classes in 1

Do you need to design a GIS map or satellite-imagery based map for your  Remote Sensing or GIS project but you don’t know how to do this?

Have you heard about  Remote Sensing object-based image analysis and machine learning or maybe QGIS or Google Earth Engine but did not know where to start with such analyses?

Do you find Remote Sensing and GIS manuals too not practical and looking for a course that takes you by hand, teach you all the concepts, and get you started on a real-life GIS mapping project?

I'm very excited that you found my Practical Geospatial Masterclass on Geospatial Data Analyses & Remote Sensing. This course provides and information that is usually delivered in 4 separate Geospatial Data Analyses & Remote Sensing courses, and thus you with learning all the necessary information to start and advance with Geospatial analysis and includes more than 9 hours of video content, plenty of practical analysis, and downloadable materials. After taking this course, you will be able to implement PRACTICAL, real-life spatial geospatial analysis and tasks, including land use and land cover mapping and change detection, machine learning for GIS, data, and maps creation, etc. in popular and FREE software tools.

This course is designed to equip you with the theoretical and practical knowledge of applied geospatial analysis, namely Remote Sensing and some Geographic Information Systems (GIS). By the end of the course, you will feel confident and completely understand the basics of Remote Sensing and GIS, learn Machine Learning applications in GIS / Remote Sensing technology, and how to use Machine Learning algorithms for various geospatial & Remote Sensing tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation, crop type mapping, etc). This course will also prepare you for using geospatial and Remote Sensing analysis with open source and free software tools.

In the course, you will be able to apply in QGIS such Machine Learning algorithms like Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. You will also learn how to download and process satellite imagery, conduct supervised and unsupervised learning, implement accuracy assessment, apply object-based image analysis, and change detection. On top of that, you will practice geospatial &  Remote Sensing analysis by completing an entire classification project by exploring the power of Machine Learning, cloud computing, and Big Data analysis using Google Erath Engine for any geographic area in the world.

In this course, I will teach you how to work with the popular open-source GIS &  Remote Sensing, i.e. QGIS software, and its great tools: Semi-Automated classification plugin and Orfeo (OTB) toolbox. You will also get introduced to cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world.

The course is ideal for professionals such as geographers, programmers, social scientists, geologists, GIS &  Remote Sensing experts, and all other experts who need to use maps in their field and would like to learn more about Machine Learning in GIS &  Remote Sensing and QGIS. If you're planning to undertake a task that requires to use a state of the art Machine Learning algorithms for creating, for instance, land cover and land use maps in QGIS and Google Earth Engine, this course will give you the confidence you need to understand and solve such geospatial problem.

One important part of the course is the practical exercises. You will be given some precise instructions on  Remote Sensing analysis, downloadable practical materials, scripts, and datasets to create maps and conduct analysis based on Machine Learning algorithms using the QGIS software and Google Earth Engine.


Geospatial Analyses & Remote Sensing : from Beginner to Pro
Geospatial Analyses & Remote Sensing : from Beginner to Pro
Geospatial Analyses & Remote Sensing : from Beginner to Pro
Geospatial Analyses & Remote Sensing : from Beginner to Pro


Introduction to the course, GIS and Remote Sensing

Introduction to the course

Applications of GIS

Applications of Remote Sensing

Software used in this course

About open-source QGIS software

Lab: QGIS installation

Semi-Automatic Classification Plugin for QGIS

Lab: QGIS interface

Lab: QGIS Toolbars

Lab: Creating account in Google Earth Engine

Basics of GIS

Definition of GIS

Main principles of GIS

Basics of Geodata and its main types

GIS software and your PC set up

Lab: Your First GIS Map in QGIS

Introduction to Remote Sensing

Definition of Remote Sensing

Introduction to digital images

Sensors and Platforms

Understanding Remote Sensing for LULC mapping

Lab: How to download satellite images with SCP plug-in

Lab: Layerstacking, True and False Colour composites

Lab: Image preprocessing - atmospheric correction

Sources of Remote Sensing images

Basics of land use and land cover (LULC) mapping and change detection in QGIS

Introduction to LULC classification based on satellite images

Supervised and unsupervised image classification

Unsupervised (K-means) image analysis in QGIS

Stages of LULC supervised classification

Lab: Training data collection

Overview of image classification algorrithms

Lab: LULC with the use of spectral angle mapping

Lab: LULC with the use of Maximum Likelihood Algorithm

Lab: LULC with the use of Minimum Distance Classification Algorithm

Accuracy assessment of LULC map

Lab: Validation data creation

Lab: Accuracy Assessment

Project: LULC mapping of Landsat 8

Image Classification in Google Earth Engine

Section Overview

Import images and their visualization in Google Earth Engine

Unsupervised (K-means) image analysis in Google Earth Engine

Random Forest Supervised CLassification in Earth Engine

Accuracy Assessment in Earth Engine

Introduction to change detection

Introduction to change detection

Lab: Change Detection in QGIS

Lab: How to make a map in QGIS

Introduction to Machine Learning in GIS

Introduction to Machine Learning

On Machine Learning in GIS and Remote Sensing

Supervised and Unsupervised Learning (classification) in GIS and Remote Sensing

Object detection in GIS

Segmentation and object-based image analysis (OBIA)

Prediction in GIS and deep learning for Big Data Analysis

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

Machine Learning and Object-based Analysis (OBIA) in GIS: an example of crop map

OTB installation

Object-based image classification (OBIA) VS pixel-based image classification

Introduction to object-based crop type classification in QGIS

Feature Extraction for object-based crop classification

Training of Machine learning Classifiers for object-based crop classification

Object-based crop classification with Machine Learning algorithms in QGIS

Machine Learning and Object-based Analysis (OBIA) in GIS: part 2

Section Overview

Segmentation of high-resolution satellite image

Creating training data from satellite image based on the segmented layer

Object-based image classification with the Machine Learning algorithm

Object-based crop classification with Machine Learning algorithms in QGIS

Section Overview

Introduction to object-based crop type mapping in QGIS

Feature Extraction for object-based crop classification

Training of Machine learning Classifiers for object-based crop classification

Object-based crop classification with Machine Learning algorithms in QGIS


Olha24 January 2021

This course has really been an eye-opener for me in Remote Sensing. I will definitely recommend this to my friends and colleagues.

Natalia11 January 2021

This course is a good start for someone who wants to learn the basics of remote sensing. Easy to understand. The teacher keeps the concept simple and brief.

Николай10 January 2021

This is a great course to gain a general overview of remote sensing. The course is clear and all concepts are well explained. The slides that accompany the course are particularly good.

Benjamin30 December 2020

The course starts strong and covers many topics very well. Further, the instructor teaches very well and in an easy to follow way. It rips through some of the advance tools, but you learn enough to use them in your applications. Occasionally, the instructor doesn't supply the correct resources, or the resources have already been transformed by the techniques being taught, so you don't get to do it with her. However, this is very minor and the instructor almost always provides more than enough examples for you to understand very well. All in all, especially if you have a decent understanding of GIS and machine learning already, this will be a good continuation to QGIS, remote sensing, google earth engine, and how to download and assess satellite imagery. The object detection was also really interesting and felt like the natural advanced progression in GIS topics! I felt confident that I could begin to apply my knowledge to professional projects, but there will still be a lot more to teach myself.

Svetlana12 October 2020

Very informative course, I have learned a lot from this course; a very detailed explanation; easily understandable; get answers to my questions in a very short time

Ульяна8 October 2020

It's very good ....and the person explaining has a thorough knowledge of the subject. thanks for this course.

Людмила2 October 2020

the provided information on Geospatial analysis, QGIS, and remote sensing are very practical and complete

Кирилюк15 September 2020

Very useful and detailed course, high-quality teaching, detailed instructions, knowledgable instructor!

Khoniker9 September 2020

This is the best course on Remote Sensing on Udemy. The video lectures are excellent prepared and the practicals make sure you understand the geospatial analysis and theory.

Khan9 September 2020

I loved this course. It started with basics and moves to advanced methods. All the concepts are perfectly explained and there are plenty of labs!

Bogdan7 September 2020

It's an amazing course for someone who wants to know the fundamentals of remote sensing and also learn more advanced spatial techniques.

Olele29 May 2020

I never knew about machine learning on QGIS and I have was so excited on seeing such. I can practical produce a map using GIS

Eric28 May 2020

Excellent course for beginners and bit experienced learner ... A very thorough and clear explanation of concepts with real-life examples. Appreciate your hard work.

Israel28 May 2020

The last video of this first section did not play perfectly. The audio stopped at around 8mins while the video continued to 9.09mins. I would have appreciated the course more if a few definitions were given and emphasized, especially GIS and Remote Sensing.


Expired5/27/2020100% OFF
Expired6/1/202094% OFF
Expired7/10/202086% OFF
Expired8/2/202090% OFF


Udemy ID


Course created date


Course Indexed date
Lee Jia Cheng
Course Submitted by

Android PlayStore
Apple Appstore