It & Software Online Course by Udemy, On Sale Here
Learn Remote Sensing, QGIS & GIS, main concepts, machine learning, QGIS classification, change detection, Earth Engine
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Geospatial Analyses & Remote Sensing: from Beginner to Pro
Geospatial Data Analyses & Remote Sensing: 5 Classes in 1Do you need to design a GIS map or satellite-imagery based map for your Remote Sensing or GIS project but you dont know how to do this?Have you heard about Remote Sensing object-based image analysis and machine learning or maybe QGIS or Google Earth Engine but did not know where to start with such analyses?Do you find Remote Sensing and GIS manuals too not practical and looking for a course that takes you by hand, teach you all the concepts, and get you started on a real-life GIS mapping project?I’m very excited that you found my Practical Geospatial Masterclass on Geospatial Data Analyses & Remote Sensing. This course provides and information that is usually delivered in 4 separate Geospatial Data Analyses & Remote Sensing courses, and thus you with learning all the necessary information to start and advance with Geospatial analysis and includes more than 9 hours of video content, plenty of practical analysis, and downloadable materials. After taking this course, you will be able to implement PRACTICAL, real-life spatial geospatial analysis and tasks, including land use and land cover mapping and change detection, machine learning for GIS, data, and maps creation, etc. in popular and FREE software tools. This course is designed to equip you with the theoretical and practical knowledge of applied geospatial analysis, namely Remote Sensing and some Geographic Information Systems (GIS). By the end of the course, you will feel confident and completely understand the basics of Remote Sensing and GIS, learn Machine Learning applications in GIS / Remote Sensing technology, and how to use Machine Learning algorithms for various geospatial & Remote Sensing tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation, crop type mapping, etc). This course will also prepare you for using geospatial and Remote Sensing analysis with open source and free software tools. In the course, you will be able to apply in QGIS such Machine Learning algorithms like Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. You will also learn how to download and process satellite imagery, conduct supervised and unsupervised learning, implement accuracy assessment, apply object-based image analysis, and change detection. On top of that, you will practice geospatial & Remote Sensing analysis by completing an entire classification project by exploring the power of Machine Learning, cloud computing, and Big Data analysis using Google Erath Engine for any geographic area in the world. In this course, I will teach you how to work with the popular open-source GIS & Remote Sensing, i.e. QGIS software, and its great tools: Semi-Automated classification plugin and Orfeo (OTB) toolbox. You will also get introduced to cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world. The course is ideal for professionals such as geographers, programmers, social scientists, geologists, GIS & Remote Sensing experts, and all other experts who need to use maps in their field and would like to learn more about Machine Learning in GIS & Remote Sensing and QGIS. If you’re planning to undertake a task that requires to use a state of the art Machine Learning algorithms for creating, for instance, land cover and land use maps in QGISand Google Earth Engine, this course will give you the confidence you need to understand and solve such geospatial problem. One important part of the course is the practical exercises. You will be given some precise instructions on Remote Sensing analysis, downloadable practical materials, scripts, and datasets to create maps and conduct analysis based on Machine Learning algorithms using the QGIS software and Google Earth Engine.
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