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Updated in [August 21st, 2023]

What skills and knowledge will you acquire during this course?
By taking this course, you will acquire the skills and knowledge to:

1. Understand the theoretical concepts of Machine Learning and Deep Learning in the context of Geographic Information Systems (GIS) and Remote Sensing.
2. Apply Machine Learning and Deep Learning algorithms, such as Random Forest, Support Vector Machines, Decision Trees, and Convolutional Neural Networks, for geospatial analysis tasks in QGIS and ArcGIS.
3. Perform land use and land cover mapping (classifications) and object-based image analysis (segmentation, object detection) using Machine Learning and Deep Learning techniques.
4. Conduct regression modeling for GIS tasks in ArcGIS.
5. Utilize open source tools like QGIS and market-leading software like ArcGIS for geospatial analysis.
6. Gain proficiency in QGIS for spatial data analysis and explore the capabilities of ArcMap and ArcGIS PRO.
7. Familiarize yourself with the Orfeo Toolbox for advanced processing in GIS.
8. Complete two independent GIS projects to practice and apply Machine Learning and Deep Learning analysis in QGIS and ArcGIS.
9. Enhance your GIS and Remote Sensing skills in a demonstrable and easy-to-follow manner.
10. Gain practical experience in creating maps based on Machine Learning algorithms using QGIS and ArcGIS software tools.

Overall, this course will equip you with the necessary skills and knowledge to confidently apply Machine Learning and Deep Learning techniques in GIS and Remote Sensing, making you proficient in advanced geospatial analysis tasks and providing you with cutting-edge geospatial methods.

How does this course contribute to professional growth?
This course contributes to professional growth by equipping individuals with theoretical and practical knowledge of Machine Learning and Deep Learning in QGIS and ArcGIS for geospatial analysis. It enables individuals to confidently apply Machine and Deep Learning algorithms for various Remote Sensing and GIS tasks, such as land use and land cover mapping, object-based image analysis, and regression modeling. The course also prepares individuals to use both open source (QGIS) and market-leading (ArcGIS) software tools, expanding their skillset and making them proficient in spatial data analysis. By completing practical exercises and independent GIS projects, individuals gain hands-on experience and the ability to implement cutting-edge geospatial methods. This course is particularly beneficial for professionals in fields such as geography, programming, social science, geology, and GIS & Remote Sensing, who need to use maps in their work and want to enhance their GIS skills with Machine Learning techniques.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education.

Course Syllabus

Introduction

Software used in this course: QGIS and ArcGIS 10.6 and ArcGIS Pro

On Machine Learning in GIS and Remote Sensing: theoretical background

Unsupervided Learning in ArcGIS

Unsupervided Learning in QGIS

Supervised Machine Learning for LULC Classification in ArcGIS

Supervised Machine Learning in QGIS

Image Segmentation in GIS

Object-based Image classification with Machine Learning algorithms in ArcGIS

Regression modelling in GIS

Getting started with Deep learning in ArcGIS Pro

Hands-on: Deep Learning in ArcGIS Pro

Make it real: Implement your own Machine Learning Project