This course is intended to furnish you with the hypothetical and useful information on Machine Learning as applied for geospatial investigation, in particular Geographic Information Systems (GIS) and Remote Sensing. Before the finish of the course, you will feel certain and totally comprehend the Machine Learning applications in GIS innovation and how to utilize Machine Learning calculations for different geospatial undertakings, for example, land use and land spread planning (characterizations) and item based picture examination (division). This course will likewise set you up for utilizing GIS with open source and free programming devices.

In the course, you will have the option to apply such Machine Learning calculations as Random Forest, Support Vector Machines and Decision Trees (and others) for characterization of satellite symbolism. On head of that, you will rehearse GIS by finishing a whole GIS venture by investigating the intensity of Machine Learning, distributed computing and Big Data examination utilizing Google Erath Engine for any geographic zone on the planet.

The course is perfect for experts, for example, geographers, software engineers, social researchers, geologists, and every other master who need to utilize maps in their field and might want to study Machine Learning in GIS. In case you’re wanting to embrace an undertaking that requires to utilize a best in class Machine Learning calculations for making, for example, land spread and land use maps, this course will give you the certainty you have to comprehend and take care of such geospatial issue.

One significant piece of the course is the down to earth works out. You will be given some exact guidelines and datasets to make maps dependent on Machine Learning calculations utilizing the QGIS programming and Google Earth Engine.

In this course, I incorporate downloadable viable materials that will educate you:

  • How to introduce open source GIS (QGIS, OTB tool stash) programming on your PC and effectively arrange it
  • QGIS programming interface including its primary segments and modules
  • Learn how to group satellite pictures with various AI calculations (arbitrary woods, bolster vector machines, choice trees, etc) in QGIS
  • Learn how to perform picture division in QGIS
  • Learn how to set up your first land spread guide utilizing the distributed computing Google Earth Engine Platform.

Who this course is for:

Geographers, software engineers, geologists, scholars, social researchers, or each other master who manages GIS maps in their fields

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