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Machine Learning in ArcGIS

Map Land Use Land Cover in GIS

Kate Alison

Land Use / Land Cover Mapping with Machine Learning and Remote Sensing Data in ArcGIS

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Land Use / Land Cover Mapping with Machine Learning and Remote Sensing Data in ArcGIS

Unlock the potential of advanced geospatial analysis with ArcGIS. This comprehensive course is designed to equip you with the knowledge and skills needed to perform sophisticated geospatial tasks, including object-based image analysis and Machine Learning for Land Use and Land Cover (LULC) mapping using ArcGIS.

Course Highlights:

  • Practical knowledge of advanced LULC mapping

  • Proficiency in ArcGIS for geospatial data analysis

  • Introduction to satellite-based image analysis for Remote Sensing

  • Application of Machine Learning algorithms for precise mapping

  • Object-based image analysis and segmentation techniques

  • Creation of change maps and accuracy assessments

  • Downloadable materials, including datasets and instructions

Course Focus:

This course is perfect for users who are familiar with ArcGIS but want to take their geospatial analysis skills to the next level. You'll gain the confidence to apply Machine Learning algorithms to LULC mapping and object-based image analysis using real-world data in ArcGIS.

Why Choose This Course:

Unlike other training resources, this course delivers practical solutions that enhance your GIS and Remote Sensing skills in a clear and easy-to-follow manner. You'll be able to tackle geospatial projects independently and impress your future employers with your advanced GIS capabilities.

What You'll Learn:

  • Advanced skills in ArcGIS for geospatial analysis

  • Understanding of Machine Learning concepts and its application in GIS

  • Classification of satellite and UAV images using various Machine Learning algorithms

  • Training, validation data collection, and accuracy assessment

  • Object-based image analysis and image segmentation techniques

  • Creation of change maps to visualize land use and land cover transformations

Enroll Today:

This course is ideal for geographers, programmers, social scientists, geologists, GIS, and Remote Sensing experts who need to create LULC maps and conduct change detection in their field. Whether you're embarking on a new geospatial project or looking to advance your skills, this course will provide you with the knowledge and confidence to excel in geospatial analysis using ArcGIS.

Enroll today and harness the full potential of geospatial analysis in ArcGIS.

Enroll now

What's inside

Learning objectives

  • Fully understand advanced gis and remote sensing methods of land use and land cover (lulc) mapping in arcgis
  • Learn how to perform such advanced gis methods as object based image analysis (obia) and object-based classification using real-world data
  • Further advanced your gis and remote sensing skills in the market leading gis software (i.e. arcgis)
  • Learn how to obtain satellite data and uav images, create training and validation data for obia and pixel-based and remote sensing data classification
  • Learn about machine learning and machine learning types / algorithms
  • Apply all stages of supervised machine learning in arcgis
  • Apply advanced machine learning image classification algorithms in arcgis
  • Explore the power of arcgis for remote sensing image analysis
  • You'll also have plenty of handy hints and tips will be provided alongside the code to prevent glitches
  • You'll have a copy of the data and some detailed manuals used in the course for your reference to use in your gis analysis
  • Learn applied remote sensing & gis skills needed for your future / current geospatial job!
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Syllabus

Introduction
Software used in this course
to introduce the students to the main concepts of machine learning in GIS & ArcGIS, to show practical examples of its applications
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Introduction to Machine Learning
On Machine Learning in GIS and Remote Sensing
Quiz
to perform unsupervised image analysis ArcGIS and learn basics of machine learning for image analysis
Basics of machine learning for Land use/Land Cover (LULC) classification
Overview of Machine Learning for Image Classification in ArcGIS
Lab: How to download satellite data in ArcGIS
Unsupervised LULC image analysis in ArcGIS
QUIZ
to perform supervised image analysis in ArcGIS and to implement accuracy assessment of the LULC maps
Common algorithms of image classification
Workflow of image classification with the Machine Learning Algorithms & Training
Lab: Creating Training data in ArcMap 10.6
Lab: Supervised image classification with Random Trees Classifier in ArcGIS
Lab: Supervised image classification with Support Vector Machines in ArcGIS
To learn how to perform Accuracy Assessment of Image CLassification in ArcGIS
Accuracy Assessment: theory
Lab: Accuracy Assessment of Classified Image in ArcGIS 10.6
To learn how to perform image segmentation in ArcGIS software
Principles of image segmentation for GIS and Remote Sensing analysis
Lab: Downloading image data for segmentation analysis in ArcGIS
Lad: Perform Image Segmentation in ArcGIS
To learn principles behind OBIA and to learn how to apply it in ArcGIS, to learn advanced LULC methods with the best GIS software on the market
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) using UAV data and SVM in ArcGIS
To conduct your final independent project on Machine Learning in ArcGIS
Final Project Description
BONUS

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Delves into advanced geospatial analysis with ArcGIS software
Suitable for professionals who want to enhance their GIS and Remote Sensing skills
Provides hands-on labs and interactive materials for practical learning
Covers advanced topics like object-based image analysis and Machine Learning algorithms
Provides downloadable materials and detailed instructions for easy reference
Requires learners to have proficiency in ArcGIS software

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Activities

Coming soon We're preparing activities for Machine Learning in ArcGIS : Map Land Use Land Cover in GIS. These are activities you can do either before, during, or after a course.

Career center

Learners who complete Machine Learning in ArcGIS : Map Land Use Land Cover in GIS will develop knowledge and skills that may be useful to these careers:
Remote Sensing Analyst
Remote Sensing Analysts use satellite and other remotely sensed data to collect information about the Earth's surface. They use this data to create maps, charts, and other visualizations that can be used to inform decision-making. This course can help Remote Sensing Analysts to develop the skills and knowledge they need to use remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
GIS Analyst
GIS Analysts use geographic information systems (GIS) software to collect, manage, and analyze spatial data. They use this data to create maps, charts, and other visualizations that can be used to inform decision-making. This course can help GIS Analysts to develop the skills and knowledge they need to use GIS software effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Urban Planner
Urban Planners develop plans for the development and use of land in urban areas. They use a variety of tools and techniques to collect and analyze data about urban areas, including GIS software and remote sensing data. This course can help Urban Planners to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Environmental Scientist
Environmental Scientists study the environment and its components, including the air, water, land, and living organisms. They use a variety of tools and techniques to collect and analyze data about the environment, including GIS software and remote sensing data. This course can help Environmental Scientists to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Transportation Planner
Transportation Planners develop plans for the development and use of transportation systems. They use a variety of tools and techniques to collect and analyze data about transportation systems, including GIS software and remote sensing data. This course can help Transportation Planners to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Cartographer
Cartographers create maps and other visualizations that represent geographic information. They use a variety of tools and techniques to create maps, including GIS software and remote sensing data. This course can help Cartographers to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Water Resources Engineer
Water Resources Engineers design and manage water resources systems. They use a variety of tools and techniques to collect and analyze data about water resources, including GIS software and remote sensing data. This course can help Water Resources Engineers to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Land Use Planner
Land Use Planners develop plans for the use of land in a particular area. They use a variety of tools and techniques to collect and analyze data about land use, including GIS software and remote sensing data. This course can help Land Use Planners to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Natural Resource Manager
Natural Resource Managers develop and implement plans for the management of natural resources, such as forests, water, and wildlife. They use a variety of tools and techniques to collect and analyze data about natural resources, including GIS software and remote sensing data. This course can help Natural Resource Managers to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Geographer
Geographers study the Earth's surface, including its physical features, climate, and human population. They use a variety of tools and techniques to collect and analyze data about the Earth, including GIS software and remote sensing data. This course can help Geographers to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
GIS Specialist
GIS Specialists use GIS software to collect, manage, and analyze spatial data. They use this data to create maps, charts, and other visualizations that can be used to inform decision-making. This course can help GIS Specialists to develop the skills and knowledge they need to use GIS software effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Geomatics Engineer
Geomatics Engineers use a variety of tools and techniques to collect and analyze data about the Earth's surface. They use this data to create maps, charts, and other visualizations that can be used to inform decision-making. This course can help Geomatics Engineers to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Data Scientist
Data Scientists use data to solve problems and make predictions. They use a variety of tools and techniques to collect, analyze, and interpret data, including GIS software and remote sensing data. This course can help Data Scientists to develop the skills and knowledge they need to use GIS software and remote sensing data effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
GIS Manager
GIS Managers oversee the development and implementation of GIS systems. They work with a variety of stakeholders to ensure that GIS systems meet the needs of the organization. This course can help GIS Managers to develop the skills and knowledge they need to manage GIS systems effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.
Geospatial Analyst
Geospatial Analysts use GIS software to collect, manage, and analyze spatial data. They use this data to create maps, charts, and other visualizations that can be used to inform decision-making. This course can help Geospatial Analysts to develop the skills and knowledge they need to use GIS software effectively, including how to perform object-based image analysis, use machine learning algorithms for image classification, and conduct accuracy assessments.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning in ArcGIS : Map Land Use Land Cover in GIS.
Provides a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a mathematical treatment of pattern recognition and machine learning, covering topics such as statistical pattern recognition, Bayesian inference, and support vector machines.
Provides a rigorous and comprehensive treatment of machine learning from a probabilistic perspective, covering topics such as Bayesian inference, graphical models, and deep learning.
Provides a comprehensive guide to supervised learning using R, covering topics such as regression, classification, and model selection.
Provides a comprehensive introduction to statistical learning methods, including supervised and unsupervised learning, and is commonly used as a textbook in academic institutions.
Provides a comprehensive overview of geospatial analysis techniques, including data acquisition, processing, and visualization. It also covers spatial statistics and modeling. The book is written in a clear and concise style, and it includes numerous examples and exercises.
Focuses on decision trees, covering topics such as tree induction, pruning, and ensemble methods.
Provides a comprehensive overview of spatial data analysis theory and applications. It covers the different types of spatial data, as well as the different methods for analyzing spatial data.
Provides a gentle introduction to machine learning using Python, covering topics such as supervised and unsupervised learning.
Provides a comprehensive overview of remote sensing and image interpretation techniques. It covers the principles of remote sensing, as well as the different types of remote sensing data and their applications. The book is written in a clear and concise style, and it includes numerous examples and exercises.
Provides a comprehensive overview of spatial analysis techniques using the R programming language. It covers data acquisition, processing, and visualization, as well as spatial statistics and modeling. The book is written in a clear and concise style, and it includes numerous examples and exercises.

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