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Lecturer(s)
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Course content
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The course content covers practical assignments for the following subject areas and topics: 1. Software tools for remote sensing data processingopen source and commercial products 2. Data processing and analysis in QGIS and SNAP 3. Remote sensing data in the optical spectrumdata processing using deep learning in ArcGIS PRO 4. Processing, visualization, and analysis of LiDAR data in ArcGIS PRO, CloudCompare, and LAStools 5. Processing and analysis of thermal camera data 6. Introduction to data processing in the microwave spectrum; processing, visualization, and analysis of data in the microwave spectrum in ArcGIS PRO
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration, Laboratory Work
- Attendace
- 70 hours per semester
- Homework for Teaching
- 10 hours per semester
- Preparation for the Course Credit
- 10 hours per semester
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Learning outcomes
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Students will learn advanced methods for processing remote sensing data using ArcGIS PRO and alternative open-source software (QGIS, SNAP, CloudCompare, LAStools). As part of their practical skills, they will master advanced methods for processing data in the optical spectrum, learn the basics of processing data in the microwave spectrum (radar remote sensing), and be able to utilise and analyse lidar and thermal imaging data.
Students can apply selected software tools and analytical methods to specific types of remote sensing data. They are also able to locate data within available online sources. Students can apply current deep learning analytical tools within the ArcGIS PRO environment.
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Prerequisites
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This course is a direct continuation of the KGG/DPZ1 subject and requires a basic knowledge of ArcGIS PRO.
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Assessment methods and criteria
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Student performance, Analysis of Activities ( Technical works)
Knowledge covering the theoretical introduction to the subject areas and the assigned reading. Skills in applying advanced remote sensing methods in specific software solutions. Regular attendance at classes. Completion of individual assignments and presentation of one's own semester project.
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Recommended literature
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ESRI Training ? webový portál zaměřený na cvičení a praktické úlohy pro ESRI produkty. .
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Doubrava P, Kvapil J., Ponocná T., Jirásková L., Řeřicha J. et al. (2015). Možnosti využití metod dálkového průzkumu a prostorových analýz pro řešení krizových situací. Praha.
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Halounová L. (2018). Dálkový průzkum Země a GIS pro sledování časových změn na Zemi = Remote sensing and GIS for change detection on the Earth. Praha.
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Lillesand T.M., Kiefer, R.W., Chipman, J.W. (2015). Remote Sensing and Image Interpretation. 7th ed.. Hoboken, NJ, USA.
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Lukeš P. (2018). Hodnocení zdravotního stavu lesních porostů v České republice pomocí satelitních dat Sentinel-2. Brandýs nad Labem.
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Procházka J. (2014). Hodnocení funkčních parametrů povrchu krajiny na územích zasažených povrchovou těžbou pomocí metod dálkového průzkumu Země (certifikovaná metodika). České Budějovice.
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Richards, J. A., Xiuping JIA. (2013). Remote Sensing Digital Image Analysis: An Introduction. Berlin.
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Richards, J.A. Remote Sensing with Imaging Radar. Heidelberg. 2009.
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Surový P., Kuželka K. (2019). Aplikace dálkového průzkumu Země v lesnictví. Praha.
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