Machine Learning in GIS and Remote Sensing: 5 Courses in 1

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This course is designed to equip you with the theoretical and practical knowledge of Machine Learning and Deep Learning in QGIS and ArcGIS as applied for geospatial analysis, namely Geographic Information Systems (GIS) and Remote Sensing. By the end of the course, you will feel confident and completely understand the Machine and Deep Learning applications in Remote Sensing & GIS technology and how to use Machine and Deep Learning algorithms for various Remote Sensing & GIS tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation, object detection) and regression modeling in QGIS and ArcGIS software. This course will also prepare you for using GIS with open source and free tools (QGIS) and a market-leading software (ArcGIS).

This course is designed to take users who use QGIS & ArcGIS for basic geospatial data/GIS/Remote Sensing analysis to perform more advanced geospatial analysis tasks including object-based image analysis using a variety of different data and applying Deep Learning & Machine Learning state of the art algorithms. In addition to making you proficient in QGIS for spatial data analysis, you will be introduced to another powerful processing toolbox – Orfeo Toolbox, and to the exciting capabilities of ArcMap and ArcGIS PRO!

In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines, Decision Trees, Convolutional Neural Networks (and others) for Remote Sensing and geospatial tasks. You will also learn how to conduct regression modeling for GIS tasks in ArcGIS. On top of that, you will practice GIS & Remote Sensing by completing two independent GIS projects by exploring the power of Machine Learning and Deep Learning analysis in QGIS and ArcGIS.