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Course Review: Prediction Mapping Using GIS Data and Advanced ML Algorithms on Coursera Overview: "Prediction Mapping Using GIS Data and Advanced ML Algorithms" is a comprehensive and cutting-edge course that bridges the fields of remote sensing, geospatial analysis, and machine learning. Taught by Omar AlThuwaynee, this course offers an in-depth exploration of how supervised classification techniques can be harnessed to address real-world environmental and societal challenges using GIS data and advanced machine learning algorithms. Course Content: This course is structured around two major projects, each illustrating powerful applications of ML in geospatial contexts: 1. Multi-label Classification: - Predicting diverse outcomes such as species distribution, air pollution levels, and complex disease risk factors. - Focused on multi-class problems where targets have multiple labeled categories, providing rich, multi-output maps. - Practical application includes the prediction of PM10 concentration, which has been published as a research article, showcasing the course’s research relevance. 2. Binary Classification: - Addressing tasks like flood mapping, land slide susceptibility, oil spill contamination, and urban heat effects. - Emphasizes the importance of considering environmental factors like topography and climate data. - Students will have the opportunity to compare results with previous courses using Artificial Neural Networks (ANNs) and assess the effectiveness of the models. Unique Features: - Utilizes the latest machine learning models, including XGBoost and Random Forest, to enhance prediction accuracy. - Employs free and publicly available remote sensing data, making it accessible for environments with limited data resources. - Provides practical tools like LaGriSU (version 2023_03_09), an open-source tool for automatically extracting training/testing data based on grid and slope units using QGIS. Why You Should Enroll: - If you are interested in applying machine learning to geospatial datasets, this course offers detailed instruction and practical examples that can be directly utilized in research or decision-making. - The course’s focus on environmental applications—such as pollution mapping, disease risk factors, and disaster susceptibility—aligns well with environmental science and urban planning fields. - For those who have previously taken related courses on neural networks, this course offers a valuable comparison to see the advantages and limitations of different approaches. - The free availability of powerful tools like LaGriSU enhances hands-on learning and project execution. Recommendation: I highly recommend "Prediction Mapping Using GIS Data and Advanced ML Algorithms" for students, researchers, and professionals interested in remote sensing, GIS, and machine learning. Its application-oriented approach, combined with real-world projects and open-source tools, makes it an excellent investment for advancing skills and producing impactful geospatial predictions. Whether you aim to publish research, support environmental decision-making, or develop new GIS applications, this course provides the knowledge and resources to succeed. Conclusion: This course stands out as one of the most advanced offerings in the domain of geospatial data analysis with machine learning. Its practical focus, combined with cutting-edge techniques and freely accessible data and tools, makes it a valuable addition to your professional skillset. Enroll now to deepen your understanding and enhance your capability in prediction mapping using GIS data and machine learning. Best regards, [Your Name]
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 regardsOmar AlThuwaynee