Mô tả

Machine Learning in GIS : Understand the Theory and Practice

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:

  • Theoretical and practical understanding of Machine Learning applications in GIS and Remote Sensing

  • Application of Machine Learning algorithms, including Random Forest, Support Vector Machines, and Decision Trees

  • Completion of a full GIS project with hands-on exercises

  • Utilization of cloud computing and Big Data analysis through Google Earth Engine

  • Ideal for professionals across various fields

  • 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:

  • Installing open-source GIS software (QGIS, OTB toolbox) and proper configuration

  • Navigating the QGIS software interface, including components and plug-ins

  • Classifying satellite images with diverse Machine Learning algorithms (e.g., Random Forest, Support Vector Machines, Decision Trees) in QGIS

  • Conducting image segmentation in QGIS

  • 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!

Bạn sẽ học được gì

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

Yêu cầu

  • A working computer

Nội dung khoá học

9 sections

Introduction to the course, GIS and Remote Sensing

4 lectures
Introduction
03:17
GIS explained
05:37
Introduction to Remote Sensing: definition
05:12
Introduction to Remote Sensing: applications
09:09

Installation of QGIS on your Computer

7 lectures
Computer Set up for GIS analysis and GIS software on the market
11:59
QGIS version information
02:21
Installing QGIS
12:39
A note on QGIS versions and it's plug-ins
08:44
Exploring QGIS interface
09:46
A power of QGIS - QGIS Plug-ins
07:45
Lab: Sign In to Google Earth Engine
03:37

Introduction to Machine Learning in GIS

3 lectures
Introduction to Machine Learning
16:03
On Machine Learning in GIS and Remote Sensing
08:19
OTB installation
02:12

Types of supervised & unsupervised machine learning and applications in GIS

8 lectures
Supervised and Unsupervised Learning (classification) in GIS and Remote Sensing
09:27
Lab: Unsupervised Image Classification in SCP
09:46
Land cover classification on the cloud using EO browser
11:29
Unsupervised (K-means) image analysis in QGIS
04:52
Random Forest supervised classification of Sentinel-2 image
25:20
Decision Trees classification of Sentinel-2 image
17:53
Accuracy Assessment
11:13
Support Vector Machine (SVM) supervised classification of the satellite imagery
2 questions

New: Image classification in QGIS: how to create training and run classification

2 lectures
Extra: Training data collection for image classification based on Landsat images
24:06
Lab: image classification in QGIS
03:51

Machine Learning in Google Earth Engine

4 lectures
EO browser for image download, spectral indices & land cover
19:15
Supervised classification with Google Earth Engine
18:57
Import images and their visualization in Google Earth Engine
16:11
Unsupervised (K-means) image analysis in Google Earth Engine
08:34

Introduction to object-based machine learning in GIS and QGIS

3 lectures
Object detection in GIS
05:31
Segmentation and object-based image analysis (OBIA)
04:42
Segmentation of high-resolution satellite image
10:26

Predictions and regression in GIS and deep learning for Big Data Analysis

4 lectures
On regression in GIS
03:29
ArcGIS Software for regression analysis
02:59
Lab: Use regression analysis in ArcGIS
25:04
Prediction in GIS and deep learning for Big Data Analysis
07:08

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

2 lectures
Project assignment
06:14
BONUS
02:12

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