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Lecturer(s)
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Popelka Stanislav, RNDr. Ph.D.
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Pechanec Vilém, prof. RNDr. Ph.D.
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Burian Jaroslav, doc. RNDr. Ph.D.
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Course content
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The course is based on a combination of lectures by experts from practice, locally lectured topics, and information channels from technology incubators. In the practical part, students will master selected aspects of the preached topics through individual assignments and group semester work. Technological innovations in the field of GIS technological innovations in the field of RS, creation of new datasets, mining existing data, new procedures and tools in geodata analysis, geodata sharing, new applications and information systems, current state and public administration projects.
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Projection (static, dynamic), Group work
- Attendace
- 46 hours per semester
- Homework for Teaching
- 46 hours per semester
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Learning outcomes
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The course introduces current trends and research issues in contemporary geoinformatics. It aims to introduce students to the recent developments in the field. The trends discussed and topics currently addressed by practitioners cover a wide range of areas shaping contemporary GIT deployment - technological innovations, new data sources and methodological approaches, ongoing standardization processes and legislative measures, challenges, and projects in government and public administration.
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Prerequisites
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Knowledge and orientation in geoinformatics within the scope of the completed professional bachelor's degree in GIT.
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Assessment methods and criteria
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Oral exam, Seminar Work, Written exam
Requirements for successful completion of the course: attendance according to the study regulations. Presentation of the term paper according to the requirements. Credit will be awarded for attendance and completion of exercises. The examination will be theoretical from the content of lectures and presentation of the term paper at the required level.
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Recommended literature
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Amazon EMR. Amazon Web Services (AWS) - Cloud Computing Service.
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Chollet, F. (2019). Deep learning v jazyku Python: knihovny Keras, Tensorflow. Praha, Grada Publishing. Knihovna programátora (Grada).
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Northup P. (2013). Using Big Data in Geographic Information Systems for Observing Earth?s Climate.
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