Machine Learning in Spatial Analysis: GIS & Remote Sensing from Udemy

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.

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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

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

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Strengthen your foundation by reviewing fundamental statistics and probability concepts to enhance your understanding of machine learning algorithms.

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Review lecture notes or textbooks
Complete practice problems
Take an online quiz or assessment

Review remote sensing and image processing textbook

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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

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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

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Stay organized by compiling and reviewing course materials regularly to strengthen your understanding and retention of key concepts.

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Create a system for organizing notes, assignments, and presentations
Review materials regularly to reinforce learning
Summarize key concepts in your own words

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Follow tutorials on using Google Earth Engine for cloud computing

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Gain hands-on experience with Google Earth Engine by following step-by-step tutorials to enhance your cloud computing skills.

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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

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Reinforce your understanding of key machine learning algorithms by completing targeted practice exercises.

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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

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Apply the principles of supervised learning to develop a model for classifying land use from remote sensing imagery.

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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 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.

See salaries and explore the career path for GIS 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.

See salaries and explore the career path for Remote Sensing 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.

See salaries and explore the career path for Geospatial Intelligence Analyst

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.

See salaries and explore the career path for Geographer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Computer Scientist

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.

See salaries and explore the career path for Cartographer

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.

See salaries and explore the career path for Water Resources Engineer

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.

See salaries and explore the career path for Urban Planner

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Environmental Scientist

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.

See salaries and explore the career path for Transportation 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.

See salaries and explore the career path for Civil 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.

See salaries and explore the career path for Software Engineer

Machine Learning in Spatial Analysis

GIS & Remote Sensing

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