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Machine Learning in GIS and Remote Sensing: 5 Courses in 1

Understand & apply machine learning and deep learning for geospatial tasks (GIS and Remote Sensing) in QGIS and ArcGIS

4.37 (97 reviews)

499

Students

7.5 hours

Content

Nov 2020

Last Update
$94.99
Regular Price

What you will learn

Fully understand the basics of Machine Learning and Machine Learning in GIS

Learn the most popular open-source GIS and Remote Sensing software tools (QGIS, SCP, OTB toolbox)

Learn the market leading GIS software ArcGIS (ArcMap) and ArcGIS Pro

Learn about supervise and unsupervised learning and their applications in GIS

Apply Machine Learning image classification in QGIS and ArcGIS

Run segmentation and object-based image analysis in QGIS and ArcGIS

Learn and apply regression modelling for GIS tasks

Understand the main developments in the field of Artificial Intelligence, deep learning and machine learning as applied to GIS

Complete two independent projects on Machine Learning and Deep Learning

Understand basics of deep learning as a part of machine learning

Apply deep learning algorithms , such as convolution neural networks, in GIS with ArcGIS Pro




Description

This course is designed to equip you with the theoretical and practical knowledge of Machine Learning and Deep Learning in QGIS and ArcGIS 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 and Deep Learning applications in Remote Sensing & GIS technology and how to use Machine and Deep Learning algorithms for various Remote Sensing & GIS tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation, object detection) and regression modeling in QGIS and ArcGIS software. This course will also prepare you for using GIS with open source and free tools (QGIS) and a market-leading software (ArcGIS).

This course is designed to take users who use QGIS & ArcGIS for basic geospatial data/GIS/Remote Sensing analysis to perform more advanced geospatial analysis tasks including object-based image analysis using a variety of different data and applying Deep Learning & Machine Learning state of the art algorithms. In addition to making you proficient in QGIS for spatial data analysis, you will be introduced to another powerful processing toolbox – Orfeo Toolbox, and to the exciting capabilities of ArcMap and ArcGIS PRO!

In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines, Decision Trees, Convolutional Neural Networks (and others) for Remote Sensing and geospatial tasks. You will also learn how to conduct regression modeling for GIS tasks in ArcGIS. On top of that, you will practice GIS & Remote Sensing by completing two independent GIS projects by exploring the power of Machine Learning and Deep Learning analysis in QGIS and ArcGIS.

This course is different from other training resources. Each lecture seeks to enhance your GIS and Remote Sensing skills in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing spatial data for your projects and gain appreciation from your future employers with your advanced GIS & Remote Sensing skills and knowledge of cutting-edge geospatial methods.

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.

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 and ArcGIS software tools.


Screenshots

Machine Learning in GIS and Remote Sensing: 5 Courses in 1
Machine Learning in GIS and Remote Sensing: 5 Courses in 1
Machine Learning in GIS and Remote Sensing: 5 Courses in 1
Machine Learning in GIS and Remote Sensing: 5 Courses in 1

Content

Introduction

Introduction

Introduction to Geographic Information Systems (GIS)

Introduction to Remote Sensing

Applications of GIS and Remote Sensing

Quiz

Software used in this course: QGIS and ArcGIS 10.6 and ArcGIS Pro

Installation of QGIS

Semi-Automatic Classification Plugin for QGIS

Intsalling plug-ins for QGIS

On Machine Learning in GIS and Remote Sensing: theoretical background

On Machine Learning in GIS and Remote Sensing: theoretical background

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

Lab: Image data acquisition in QGIS

Common algorithms of image classification

Regression Analysis

Prediction in GIS and deep learning for Big Data Analysis

Unsupervided Learning in ArcGIS

Overview of Machine Learning for Image Classification in ArcGIS

ArcGIS Software

Unsupervised LULC image analysis in ArcGIS

Unsupervided Learning in QGIS

Installing OTB plug-in for QGIS

Unsupervised (K-means) image analysis in QGIS

Supervised Machine Learning for LULC Classification in ArcGIS

Stages of LULC supervised classification

Lab: Creating Training data in ArcMap 10.6

Lab: Supervised image classification with Support Vector Machines in ArcGIS

Supervised Machine Learning in QGIS

Lab: Supervided Learning based on Maximum Likelihood Algorithm

Lab: LULC with the use of Minimum Distance Classification Algorithm

Accuracy assessment of the map in QGIS

Lab: Validation data creation

Lab: Accuracy Assessment of LULC map in QGIS

Random Forest supervised classification of Sentinel-2 image in QGIS

Comparison of Random Forest and Decision Trees Classifier resilts

Image Segmentation in GIS

Principles of image segmentation for GIS and Remote Sensing analysis

Lab: Downloading image data for segmentation analysis

Lad: Perform Image Segmentation in ArcGIS

Lab: Segmentation of satellite image in QGIS

Object-based Image classification with Machine Learning algorithms in ArcGIS

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

Creating training data for object-based image classification in ArcGIS

Object-based image classification (OBIA) in ArcGIS

Regression modelling in GIS

Regression Model: theory

OSL modelling in GIS

OSL modelling in ArcGIS

Getting started with Deep learning in ArcGIS Pro

Deep Learning in ArcGIS Pro

Introduction to neural networks

Deep learning in ArcGIS Pro: an overview

Getting started with Deep learning in ArcGIS Pro

Hands-on: Deep Learning in ArcGIS Pro

Software used in this section: ArcGIS Pro

Training data creation for convolutional (or deep) neural network (CNN)

Lab: Image preparation for deep learning in ArcGIS Pro

Lab: Install deep learning frameworks for ArcGIS

Deep Learning (CNN) model definition in ArcGIS PRO

Lab: Deep Learning (CNN) model definition in ArcGIS PRO

Apply deep learning model for object detection or image classification

Lab: Detect image object with CNN (deep learning model) in ArcGIS Pro

Summary

Make it real: Implement your own Machine Learning Project

Project 1: Supervised Learning for classification of Landsat data in QGIS

Project 2: Deep Learning in ArcGIS Pro

BONUS



Reviews

F
Fahad30 December 2020

I really enjoyed the whole class and I understand each point. The instructor gave a wonderful class experience. Nice presentation. She makes difficult concepts of Remote Sensing and MAchine Learning understand easily.

F
Fatima30 December 2020

It was a great learning experience, and it has all explanation on the theory and advanced techniques. Also lots of practicals to practice.

О
Олеся16 December 2020

Excellent course. Very cool teacher, great content of the course on Machine Learning in GIS and Remote Sensing, perfect pronunciation (specially for non-active speakers), and very clear explanations. Totally satisfied.

Е
Елена16 December 2020

Its a very good course for everybody. For ones who are new to satellite remote sensing and also for pro who want to brush up their skill.

Т
Татьяна16 December 2020

Its a fantastic course. I gained invaluable information in a easy-to-understand method. very useful for me. Thank you!

О
Ольга15 December 2020

Its a must for everybody. I really enjoyed the whole class and i understand each point.The instructor gave a wonderful class experience. Nice presentation. she makes difficult concepts understand easily.

Н
Нина19 November 2020

This is the best course I have ever taken. Learned a lot from it. The lectures are excellent. Problem sets are challenging. I highly recommend this course for everyone who works with GIS and Remote Sensing!

А
Алия18 November 2020

It was very simple and easy to follow! I am truly amazed by how much I learned about MAchine Learning and GIS in such a short amount of time!

Y
Yuliya17 November 2020

Great introductory course to the basics of ML in GIS! Everything is well explained and the materials are on point.

S
Sabbir29 October 2020

This course is perfect - well structured, easily understandable, and just the right proportion of theory and practical exercise. I'm glad to have learned the principles of Machine Learning - and looking forward to learning more. Thank you.

K
Khoniker29 October 2020

Great course. Really informative, good tone and speed and I really learned a lot about machine learning in GIS and Remote Sensing in QGIS and ArcGIS.

М
Милена12 October 2020

This is a very good intro course on GIS. The main focus is working with QGis and making a variety of land cover/land use maps using machine learning approaches.

К
Кирилюк25 September 2020

The course is very interesting. I would recommend that beginners and experts in Remote Sensing both can take this course

B
Bogdan6 September 2020

It was a great learning experience, and it has all explanation on the theory and advanced techniques. Also lots of practicals to practice.

J
Jessie8 July 2020

That's a very informative course that takes you from the theory of machine learning and deep learning to its applications for spatial tasks - great course!


Coupons

StatusDateDiscount
Expired7/4/202095% OFF
Expired9/1/202092% OFF
Expired2/17/202195% OFF


3287766

Udemy ID

7/1/2020

Course created date

7/4/2020

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