Machine Learning in GIS : Understand the Theory and Practice
Apply machine learning in Geographic information systems and Remote Sensing in QGIS and Google Earth Engine
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Are you eager to harness the power of Machine Learning for geospatial analysis, but not sure where to start? Welcome to our course, designed to equip you with the theoretical and practical knowledge of Machine Learning applied in the fields of Geographic Information Systems (GIS) and Remote Sensing. Whether you’re interested in land use and land cover mapping, classifications, or object-based image analysis, this course has you covered.
Course Highlights:
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Theoretical and practical understanding of Machine Learning applications in GIS and Remote Sensing
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Application of Machine Learning algorithms, including Random Forest, Support Vector Machines, and Decision Trees
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Completion of a full GIS project with hands-on exercises
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Utilization of cloud computing and Big Data analysis through Google Earth Engine
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Ideal for professionals across various fields
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Step-by-step instructions and downloadable practical materials
Course Focus:
This comprehensive course delves into the realm of Machine Learning in geospatial analysis, offering a blend of theory and practical application. Upon course completion, you will possess the knowledge and confidence to harness Machine Learning for a wide range of geospatial tasks.
What You’ll Learn:
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Installing open-source GIS software (QGIS, OTB toolbox) and proper configuration
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Navigating the QGIS software interface, including components and plug-ins
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Classifying satellite images with diverse Machine Learning algorithms (e.g., Random Forest, Support Vector Machines, Decision Trees) in QGIS
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Conducting image segmentation in QGIS
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Preparing your inaugural land cover map using the cloud computing platform Google Earth Engine
Who Should Enroll:
This course caters to a diverse audience, including geographers, programmers, social scientists, geologists, and any professionals who employ maps in their respective fields. If you anticipate tasks that demand state-of-the-art Machine Learning algorithms for tasks like land cover and land use mapping, this course empowers you with the skills to address such geospatial challenges.
INCLUDED IN THE COURSE: Gain access to step-by-step instructions, practical materials, datasets, and guidance for hands-on exercises in QGIS and Google Earth Engine. Enroll today to unlock the potential of Machine Learning for geospatial analysis!
Installation of QGIS on your Computer
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1
IntroductionIn 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.
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2
GIS explained
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.
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3
Introduction to Remote Sensing: applications
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.
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4
Introduction to Remote Sensing: definition
In this class, you will learn a definition of Remote Sensing.
Introduction to Machine Learning in GIS
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5
Computer Set up for GIS analysis and GIS software on the market
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.
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Installing QGIS
In this video, I will show you how to install QGIS on your computer.
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7
Exploring QGIS interface
In this video, we will explore together the QGIS interface.
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8
A power of QGIS - QGIS Plug-ins
In this video, I will show you how to install and manage QGIS plug-ins.
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9
Lab: Sign In to Google Earth Engine
Types and applications of machine learning in GIS and Remote Sensing
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10
Introduction to Machine Learning
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.
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11
On Machine Learning in GIS and Remote Sensing
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.
Machine Learning in GIS: Hands-On
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12
Supervised and Unsupervised Learning (classification) in GIS and Remote Sensing
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.
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13
Object detection in GIS
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.
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14
Segmentation and object-based image analysis (OBIA)
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.
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15
Prediction in GIS and deep learning for Big Data Analysis
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.
Unsupervised Image Classification in Google Earth Engine
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16
OTB installation
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.
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17
Unsupervised (K-means) image analysis in QGIS
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18
Random Forest supervised classification of Sentinel-2 image
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.
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Decision Trees classification of Sentinel-2 image
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.
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Accuracy Assessment
IN this video lecture, you will learn how o perform accuracy assessment for a case of supervised classification.
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Support Vector Machine (SVM) supervised classification of the satellite imagery -
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Segmentation of high-resolution satellite image
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.
Final Project: Machine Learning for GIS on cloud (Google Earth Engine)
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Course details
Video
6 hours
Certificate of Completion
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