In this course, four machine learning supervised classification based techniques used with remote sensing and geospatial resources data to predict two different types of applications:
Project 1: Data of Multi-labeled target prediction via multi-label classification (multi class problem). Target (Y) that has 3 labeled classes (instead of Numbers): Names, description, ordinal value (small, large, X-large)..Multiple output maps. Like:
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In this course, four machine learning supervised classification based techniques used with remote sensing and geospatial resources data to predict two different types of applications:
Project 1: Data of Multi-labeled target prediction via multi-label classification (multi class problem). Target (Y) that has 3 labeled classes (instead of Numbers): Names, description, ordinal value (small, large, X-large)..Multiple output maps. Like:
In this course, four machine learning supervised classification based techniques used with remote sensing and geospatial resources data to predict two different types of applications:
Project 1: Data of Multi-labeled target prediction via multi-label classification (multi class problem). Target (Y) that has 3 labeled classes (instead of Numbers): Names, description, ordinal value (small, large, X-large)..Multiple output maps. Like:
Increase specific type of species in certain areas and its relationship with surrounding conditions.
Air pollution limits prediction (Good, moderate, unhealthy, Hazardous..)
Complex diseases types: potential risk factors and their effects on the disease are investigated to identify risk factors that can be used to develop prevention or intervention strategies.
Course application: Prediction of concentration of particulate matter of less than 10 µm diameter (PM10)
This project was published as research articles using similar materials and with major part of analysis (with slight modification to the code). "Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms" in Environmental Science and Pollution Research journal.
Project 2: Data of Binary labeled target prediction. Target with 2 classes: Yes and No, Slides and No slide, Happened –Not happened, Contaminated- Clean.
Flooded areas and it contribution factors like topographic and climate data.
Climate change related consequences and its dragging factors like urban heat islands and it relationship with land uses.
Oil spills: polluted and non polluted.
Course application: Landslide susceptibility mapping in prone area.
If you are previously enrolled in my previous course using ANN, then you have the chance to compare the outcomes, as we used the same landslide data here.
Eventually, all the measured data (training and testing), were used to produce the prediction map to be used in further GIS analysis or directly to be presented to decision makers or writing research article in SCI journals.
This course considered the most advanced, in terms of analysis models and output maps that successfully invested in the (1) machine learning algorithm and geospatial domains; (2) free available data of remote sensing in data scarce environment.
IMPORTANT:
LaGriSU Version 2023_03_09 is available (Free) to download using Github link
(search for /Althuwaynee/LaGriSU_Landslide-Grid-and-Slope-Units-QGIS_ToolPack)
*LaGriSU (automatic extraction of training / testing thematic data using Grid and Slope units)
Best regards
Omar AlThuwaynee
Here's a deal for you
UDEAFNULP2024
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Activities
Review and Organize Course Materials
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Strengthen your understanding by reviewing, organizing, and summarizing course notes, assignments, quizzes, and exams.
Show steps
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Compile and organize notes from lectures, readings, and other course materials.
-
Create summaries of key concepts and ideas.
Attend Industry Meetups and Conferences on Machine Learning and Geospatial Technologies
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Connect with professionals in the field, learn about industry trends, and stay up-to-date on the latest advancements in machine learning and geospatial technologies.
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Networking
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Identify relevant industry meetups and conferences.
-
Register for and attend these events.
-
Network with attendees and exchange knowledge and ideas.
Form Study Groups with Classmates
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Enhance your understanding by discussing course concepts, sharing knowledge, and working together on assignments with classmates.
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Identify classmates with similar interests and learning styles.
-
Schedule regular study sessions to discuss course materials, work on assignments, and prepare for exams.
Four other activities
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Explore Advanced Techniques in Machine Learning for Geospatial Data Analysis
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Expand your knowledge by learning about cutting-edge machine learning techniques specifically tailored for geospatial data analysis.
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-
Identify online tutorials and resources covering advanced machine learning techniques for geospatial data analysis.
-
Follow the tutorials to gain practical experience in implementing these techniques.
-
Apply the acquired knowledge to enhance your understanding of the course materials.
Practice Classification Algorithms
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Reinforce your understanding of classification algorithms by working through practice problems and exercises.
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Classification Algorithms
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-
Solve practice problems on a variety of classification datasets.
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Experiment with different classification algorithms to observe their performance on different types of datasets.
-
Analyze the results of your experiments to identify strengths and weaknesses of different algorithms.
Develop a Machine Learning Model for Predicting PM10 Concentration
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Gain hands-on experience in developing a machine learning model that can predict PM10 concentration using remote sensing data.
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-
Gather and preprocess remote sensing and PM10 concentration data.
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Train and evaluate different machine learning algorithms for PM10 concentration prediction.
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Select the best performing model and deploy it as a predictive tool.
Contribute to Open Source Projects in Machine Learning for Geospatial Analysis
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Gain practical experience and contribute to the community by participating in open source projects related to machine learning for geospatial analysis.
Browse courses on
Machine Learning
Show steps
-
Identify open source projects in the field of machine learning for geospatial analysis.
-
Choose a project to contribute to and familiarize yourself with its codebase.
-
Propose and implement improvements or new features to the project.
Review and Organize Course Materials
Show steps
Strengthen your understanding by reviewing, organizing, and summarizing course notes, assignments, quizzes, and exams.
Show steps
- Compile and organize notes from lectures, readings, and other course materials.
- Create summaries of key concepts and ideas.
Attend Industry Meetups and Conferences on Machine Learning and Geospatial Technologies
Show steps
Connect with professionals in the field, learn about industry trends, and stay up-to-date on the latest advancements in machine learning and geospatial technologies.
Browse courses on
Networking
Show steps
- Identify relevant industry meetups and conferences.
- Register for and attend these events.
- Network with attendees and exchange knowledge and ideas.
Form Study Groups with Classmates
Show steps
Enhance your understanding by discussing course concepts, sharing knowledge, and working together on assignments with classmates.
Show steps
- Identify classmates with similar interests and learning styles.
- Schedule regular study sessions to discuss course materials, work on assignments, and prepare for exams.
Explore Advanced Techniques in Machine Learning for Geospatial Data Analysis
Show steps
Expand your knowledge by learning about cutting-edge machine learning techniques specifically tailored for geospatial data analysis.
Show steps
- Identify online tutorials and resources covering advanced machine learning techniques for geospatial data analysis.
- Follow the tutorials to gain practical experience in implementing these techniques.
- Apply the acquired knowledge to enhance your understanding of the course materials.
Practice Classification Algorithms
Show steps
Reinforce your understanding of classification algorithms by working through practice problems and exercises.
Browse courses on
Classification Algorithms
Show steps
- Solve practice problems on a variety of classification datasets.
- Experiment with different classification algorithms to observe their performance on different types of datasets.
- Analyze the results of your experiments to identify strengths and weaknesses of different algorithms.
Develop a Machine Learning Model for Predicting PM10 Concentration
Show steps
Gain hands-on experience in developing a machine learning model that can predict PM10 concentration using remote sensing data.
Show steps
- Gather and preprocess remote sensing and PM10 concentration data.
- Train and evaluate different machine learning algorithms for PM10 concentration prediction.
- Select the best performing model and deploy it as a predictive tool.
Contribute to Open Source Projects in Machine Learning for Geospatial Analysis
Show steps
Gain practical experience and contribute to the community by participating in open source projects related to machine learning for geospatial analysis.
Browse courses on
Machine Learning
Show steps
- Identify open source projects in the field of machine learning for geospatial analysis.
- Choose a project to contribute to and familiarize yourself with its codebase.
- Propose and implement improvements or new features to the project.
Career center
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Environmental Scientist
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Planning Director
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Cartographer
Geographic Information Systems (GIS) Manager
Data Scientist
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Water Resources Specialist
Land Surveyor
Geographic Information Systems (GIS) Technician
Environmental Planner
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