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Machine Learning in GIS : Understand the Theory and Practice
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Machine Learning in GIS : Understand the Theory and Practice
Read more
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 (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.
Enroll today to unlock the potential of Machine Learning for geospatial analysis.
Register for this course and see more details by visiting:
OpenCourser.com/course/yo1vtt/machine
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Employs hands-on exercises to develop practical skills in Machine Learning for geospatial analysis
Offers instruction on applying Machine Learning algorithms in GIS and Remote Sensing through Random Forest, Support Vector Machines, and Decision Trees
Well-suited for professionals across various fields who seek to enhance their geospatial analysis capabilities
Effectively combines theoretical knowledge with practical application, equipping learners to tackle real-world challenges
Leverages Google Earth Engine for cloud computing and Big Data analysis, ensuring learners are proficient in cutting-edge technologies
Provides guidance from course instructors, who are experts in the field and offer valuable insights
Save this course
Save Machine Learning in Spatial Analysis: GIS & Remote Sensing to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning in Spatial Analysis: GIS & Remote Sensing with these
activities:
Review basic statistics and probability concepts
Show steps
Strengthen your foundation by reviewing fundamental statistics and probability concepts to enhance your understanding of machine learning algorithms.
Browse courses on
Statistics
Show steps
-
Review lecture notes or textbooks
-
Complete practice problems
-
Take an online quiz or assessment
Review remote sensing and image processing textbook
Show steps
Review the fundamental concepts of remote sensing and image interpretation to strengthen your foundation for the course.
View
Remote Sensing and Image Interpretation
on Amazon
Show steps
-
Read chapters 1-3
-
Complete the practice exercises at the end of each chapter
-
Summarize the key concepts in your own words
Organize and review course materials
Show steps
Stay organized by compiling and reviewing course materials regularly to strengthen your understanding and retention of key concepts.
Show steps
-
Create a system for organizing notes, assignments, and presentations
-
Review materials regularly to reinforce learning
-
Summarize key concepts in your own words
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on using Google Earth Engine for cloud computing
Show steps
Gain hands-on experience with Google Earth Engine by following step-by-step tutorials to enhance your cloud computing skills.
Browse courses on
Cloud Computing
Show steps
-
Sign up for a Google Earth Engine account
-
Follow the official Google Earth Engine tutorials
-
Experiment with different datasets and algorithms
Complete practice exercises on Random Forest, Support Vector Machines, and Decision Trees
Show steps
Reinforce your understanding of key machine learning algorithms by completing targeted practice exercises.
Browse courses on
Random Forest
Show steps
-
Find a set of practice exercises or problems
-
Attempt to solve the exercises on your own
-
Check your solutions against provided answers or online resources
Develop a land use classification model using supervised learning
Show steps
Apply the principles of supervised learning to develop a model for classifying land use from remote sensing imagery.
Browse courses on
Supervised Learning
Show steps
-
Acquire a dataset of labeled land use imagery
-
Preprocess the imagery and extract relevant features
-
Select and train a supervised learning algorithm
-
Evaluate the performance of your model
-
Deploy your model to predict land use for new imagery
Review basic statistics and probability concepts
Show steps
Strengthen your foundation by reviewing fundamental statistics and probability concepts to enhance your understanding of machine learning algorithms.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review lecture notes or textbooks
- Complete practice problems
- Take an online quiz or assessment
Review remote sensing and image processing textbook
Show steps
Review the fundamental concepts of remote sensing and image interpretation to strengthen your foundation for the course.
View
Remote Sensing and Image Interpretation
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3
- Complete the practice exercises at the end of each chapter
- Summarize the key concepts in your own words
Organize and review course materials
Show steps
Stay organized by compiling and reviewing course materials regularly to strengthen your understanding and retention of key concepts.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and presentations
- Review materials regularly to reinforce learning
- Summarize key concepts in your own words
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on using Google Earth Engine for cloud computing
Show steps
Gain hands-on experience with Google Earth Engine by following step-by-step tutorials to enhance your cloud computing skills.
Browse courses on
Cloud Computing
Show steps
Show steps
Show steps
- Sign up for a Google Earth Engine account
- Follow the official Google Earth Engine tutorials
- Experiment with different datasets and algorithms
Complete practice exercises on Random Forest, Support Vector Machines, and Decision Trees
Show steps
Reinforce your understanding of key machine learning algorithms by completing targeted practice exercises.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Find a set of practice exercises or problems
- Attempt to solve the exercises on your own
- Check your solutions against provided answers or online resources
Develop a land use classification model using supervised learning
Show steps
Apply the principles of supervised learning to develop a model for classifying land use from remote sensing imagery.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Acquire a dataset of labeled land use imagery
- Preprocess the imagery and extract relevant features
- Select and train a supervised learning algorithm
- Evaluate the performance of your model
- Deploy your model to predict land use for new imagery
Career center
Learners who complete Machine Learning in Spatial Analysis: GIS & Remote Sensing will develop knowledge and skills
that may be useful to these careers:
GIS Analyst
GIS Analyst
GIS Analysts provide expertise in geographic information systems (GIS) to solve complex spatial problems. Machine Learning is increasingly used in GIS to automate tasks, extract insights, and improve decision-making, making this course a perfect fit for those in this field or looking to enter it.
Remote Sensing Analyst
Remote Sensing Analyst
Remote Sensing Analysts use satellite imagery and other remotely sensed data to extract information about the Earth's surface. Machine Learning is increasingly used in remote sensing to automate analysis, identify patterns, and enhance data quality, making this course highly relevant for those in this field or looking to enter it.
Geospatial Intelligence Analyst
Geospatial Intelligence Analyst
Geospatial Intelligence Analysts utilize geospatial technologies for analysis and problem-solving. Machine Learning is an increasingly important aspect of a Geospatial Intelligence Analyst's work in automating analysis and extracting insights from vast geospatial data sets. This course provides a great foundation in ML theory and practice for those in this field or looking to enter it.
Geographer
Geographer
Geographers study the Earth's surface and its human and natural environments. Machine Learning is increasingly used in geography to model spatial relationships, predict outcomes, and make better decisions, making this course relevant for those in this field or looking to enter it.
Data Scientist
Data Scientist
Data Scientists use machine learning and other techniques to extract insights from data. Geospatial data is a large and growing source of data for Data Scientists, and this course can help those in this field or looking to enter it develop the skills to work with geospatial data.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract insights and inform decision-making. Machine Learning is increasingly used in data analysis to automate analysis, identify patterns, and make better decisions, making this course relevant for those in this field or looking to enter it.
Computer Scientist
Computer Scientist
Computer Scientists conduct research in the field of computer science. Machine Learning is a subfield of computer science, and this course can help those in this field or looking to enter it develop the skills to work with Machine Learning.
Cartographer
Cartographer
Cartographers create maps and other visual representations of geographic information. Machine Learning is increasingly used in cartography to automate map creation, enhance map quality, and improve decision-making, making this course relevant for those in this field or looking to enter it.
Water Resources Engineer
Water Resources Engineer
Water Resources Engineers design and manage water resources systems, such as dams, reservoirs, and canals. Machine Learning is increasingly used in water resources engineering to model water systems, predict outcomes, and make better decisions, making this course relevant for those in this field or looking to enter it.
Urban Planner
Urban Planner
Urban Planners develop plans for the development and use of land in urban areas. Machine Learning is increasingly used in urban planning to model urban systems, predict outcomes, and make better decisions, making this course relevant for those in this field or looking to enter it.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to solve problems in finance, business, and other fields. Machine Learning is increasingly used in quantitative analysis to build models, predict outcomes, and make better decisions, making this course relevant for those in this field or looking to enter it.
Environmental Scientist
Environmental Scientist
Environmental Scientists study the environment and its interactions with humans. Machine Learning is increasingly used in environmental science to model environmental systems, predict outcomes, and make better decisions, making this course relevant for those in this field or looking to enter it.
Transportation Engineer
Transportation Engineer
Transportation Engineers design and manage transportation systems, such as roads, railways, and airports. Machine Learning is increasingly used in transportation engineering to model transportation systems, predict outcomes, and make better decisions, making this course relevant for those in this field or looking to enter it.
Civil Engineer
Civil Engineer
Civil Engineers design and build infrastructure, such as roads, bridges, and buildings. Machine Learning is increasingly used in civil engineering to model structural systems, predict outcomes, and make better decisions, making this course relevant for those in this field or looking to enter it.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Machine Learning is increasingly used in software engineering to build intelligent systems, improve performance, and make better decisions, making this course relevant for those in this field or looking to enter it.
For more career information including salaries, visit:
OpenCourser.com/course/yo1vtt/machine
Reading list
We've selected nine 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 Spatial Analysis: GIS & Remote Sensing.
Comprehensive introduction to pattern recognition and machine learning. It covers a broad range of topics, from supervised and unsupervised learning to Bayesian methods. The book provides a deep understanding of the theoretical foundations of machine learning.
Comprehensive introduction to reinforcement learning. It covers a broad range of topics, from basic concepts to advanced techniques. The book provides a deep understanding of the theoretical foundations and practical applications of reinforcement learning.
Save
Comprehensive introduction to deep learning. It covers a broad range of topics related to deep learning, from basic concepts to advanced techniques. The book provides a deep understanding of the theoretical foundations and practical applications of deep learning.
Explores the theoretical foundations and practical applications of Gaussian processes. Gaussian processes are a powerful machine learning technique used in a wide range of applications, including regression, classification, and reinforcement learning.
Standard textbook for remote sensing. It offers a comprehensive overview of the principles and techniques of remote sensing, which complements the course’s focus on integrating remote sensing data. The book is particularly useful for background knowledge on remote sensing.
Save
Provides a comprehensive introduction to geospatial analysis, which is relevant to the course’s focus on spatial analysis. The book discusses the theoretical underpinnings and techniques of geospatial analysis and can complement the course’s theoretical background on geospatial analysis.
Offers a gentle introduction to Machine Learning, assuming no prior knowledge of machine learning or mathematics. The book provides a practical approach to applying machine learning techniques and can serve as a foundation for the course’s more advanced topics in machine learning.
Comprehensive introduction to Bayesian data analysis. It covers all the basic concepts of Bayesian statistics, and it provides numerous examples of how to apply the techniques in practice.
Practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. The book covers all the basic concepts of machine learning and provides numerous examples of how to apply the techniques in practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yo1vtt/machine
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Effort
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Good to know
Employs hands-on exercises to develop practical skills in Machine Learning for geospatial analysis
Offers instruction on applying Machine Learning algorithms in GIS and Remote Sensing through Random Forest, Support Vector Machines, and Decision Trees
Well-suited for professionals across various fields who seek to enhance their geospatial analysis capabilities
Effectively combines theoretical knowledge with practical application, equipping learners to tackle real-world challenges
Leverages Google Earth Engine for cloud computing and Big Data analysis, ensuring learners are proficient in cutting-edge technologies
Provides guidance from course instructors, who are experts in the field and offer valuable insights
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