MEGA Machine Learning in GIS & Remote Sensing: 5 Courses in1

Understand & apply machine learning and deep learning for geospatial tasks (GIS and Remote Sensing) in QGIS and ArcGIS

4.16 (490 reviews)

Udemy

platform

English

language

Other

What you will learn

Fully understand the basics of Machine Learning and Machine Learning in GIS

Learn the most popular open-source GIS and Remote Sensing software tools (QGIS, SCP, OTB toolbox)

Learn the market leading GIS software ArcGIS (ArcMap) and ArcGIS Pro

Learn about supervise and unsupervised learning and their applications in GIS

Apply Machine Learning image classification in QGIS and ArcGIS

Run segmentation and object-based image analysis in QGIS and ArcGIS

Learn and apply regression modelling for GIS tasks

Understand the main developments in the field of Artificial Intelligence, deep learning and machine learning as applied to GIS

Complete two independent projects on Machine Learning and Deep Learning

Understand basics of deep learning as a part of machine learning

Apply deep learning algorithms , such as convolution neural networks, in GIS with ArcGIS Pro

Course Gallery

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MEGA Machine Learning in GIS & Remote Sensing: 5 Courses in1 – Screenshot 3 — Screenshot 3 – MEGA Machine Learning in GIS & Remote Sensing: 5 Courses in1

MEGA Machine Learning in GIS & Remote Sensing: 5 Courses in1 – Screenshot 4 — Screenshot 4 – MEGA Machine Learning in GIS & Remote Sensing: 5 Courses in1

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Our Verdict

MEGA Machine Learning in GIS & Remote Sensing: 5 Courses in1 is a comprehensive program offering insights into machine learning and deep learning techniques tailored to GIS and Remote Sensing tasks. The instructor's expertise and effective delivery contribute to an engaging learning experience. However, certain concerns include inconsistencies in QGIS-related sections, abrupt changes in content organization, and unresolved errors in practical exercises. Considering the course updates through 2024, addressing these issues could significantly improve overall learning satisfaction and outcomes.

What We Liked

Covers machine learning and deep learning applications specific to GIS & Remote Sensing
Instructor is knowledgeable, engaging, and delivers the content effectively
Provides insights into both open-source (QGIS, SCP, OTB toolbox) and proprietary (ArcGIS) software tools
Comprehensive curriculum includes supervised and unsupervised learning, image classification, segmentation, object-based image analysis, regression modeling, and deep learning algorithms like CNNs

Potential Drawbacks

Some users have experienced version issues and errors in QGIS-related sections
Fast-paced theoretical lessons with limited slides might be challenging for some students to follow
Lack of structured explanation when unsuccessful results are encountered during the classification process
Occasional interruptions, incomplete videos, and abrupt topic changes may cause confusion
Limited availability of promised files and datasets hinders practical exercise completion