English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5 Hours | 2.91 GB
Understand & apply machine learning in Geographic information systems and Remote Sensing in QGIS and Google Earth Engine
This course is designed to equip you with the theoretical and practical knowledge of Machine Learning as applied for geospatial analysis, namely Geographic Information Systems (GIS) and Remote Sensing. By the end of the course, you will feel confident and completely understand the Machine Learning applications in GIS technology and how to use Machine Learning algorithms for various geospatial tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation). This course will also prepare you for using GIS with open source and free software tools.
In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. On top of that, you will practice GIS by completing an entire GIS 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.
The course is ideal for professionals such as geographers, programmers, social scientists, geologists, and all other experts who need to use maps in their field and would like to learn more about Machine Learning in GIS. 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, 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 and datasets to create maps based on Machine Learning algorithms using the QGIS software and Google Earth Engine.
In this course, I include downloadable practical materials that will teach you:
- How to install open source GIS (QGIS, OTB toolbox) software on your computer and correctly configure it
- QGIS software interface including its main components and plug-ins
- Learn how to classify satellite images with different machine learning algorithms (random forest, support vector machines, decision trees and so on) in QGIS
- Learn how to perform image segmentation in QGIS
- Learn how to prepare your first land cover map using the cloud computing Google Earth Engine Platform.
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
Table of Contents
Introduction to the course, GIS and Remote Sensing
1 Introduction
2 GIS explained
3 Introduction to Remote Sensing definition
4 Introduction to Remote Sensing applications
Installation of QGIS on your Computer
5 Computer Set up for GIS analysis and GIS software on the market
6 Installing QGIS
7 Exploring QGIS interface
8 A power of QGIS – QGIS Plug-ins
9 Lab Sign In to Google Earth Engine
Introduction to Machine Learning in GIS
10 Introduction to Machine Learning
11 On Machine Learning in GIS and Remote Sensing
12 OTB installation
Types of supervised & unsupervised machine learning and applications in GIS
13 Supervised and Unsupervised Learning (classification) in GIS and Remote Sensing
14 Unsupervised (K-means) image analysis in QGIS
15 Random Forest supervised classification of Sentinel-2 image
16 Decision Trees classification of Sentinel-2 image
17 Accuracy Assessment
New Image classification in QGIS how to create training and run classification
18 Extra Training data collection for image classification based on Landsat images
19 Lab image classification in QGIS
Machine Learning in Google Earth Engine
20 Supervised classification with Google Earth Engine
21 Import images and their visualization in Google Earth Engine
22 Unsupervised (K-means) image analysis in Google Earth Engine
Introduction to object-based machine learning in GIS and QGIS
23 Object detection in GIS
24 Segmentation and object-based image analysis (OBIA)
25 Segmentation of high-resolution satellite image
Predictions and regression in GIS and deep learning for Big Data Analysis
26 On regression in GIS
27 ArcGIS Software for regression analysis
28 Lab Use regression analysis in ArcGIS
29 Prediction in GIS and deep learning for Big Data Analysis
Final Project Machine Learning for GIS on cloud (Google Earth Engine)
30 Project assignment
31 BONUS
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