548-0083/05 – GeoComputation (GC)

Gurantor departmentDepartment of GeoinformaticsCredits5
Subject guarantorprof. Ing. Jiří Horák, Dr.Subject version guarantorprof. Ing. Jiří Horák, Dr.
Study levelundergraduate or graduateRequirementCompulsory
Study languageEnglish
Year of introduction2021/2022Year of cancellation
Intended for the facultiesHGFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
HOR10 prof. Ing. Jiří Horák, Dr.
JUR02 Ing. Lucie Orlíková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 8+8

Subject aims expressed by acquired skills and competences

The objective is to learn student how to use basic methods of artificial intelligence namely machine learning such as decision trees, support vector machines and neural analysis in geoinformatics, explain them pronciples and methods of data mining, theory of chaos and fractals, and selected methods of stochastic spatial simulations.

Teaching methods



The subject introduces basic approaches and methods of artificial intelligence, especially machine learning and focus on their utilization in geoinformatics, where it is necessary to evaluate spatial properties, to adapt spatial sampling, and perform appropriate data transformation. Classification methods such as Bayes classifiers, decision trees, support vector machines. Variants for regression analysis. Neural network, including advanced methods such as deep learning and convolution neural network. The further part demonstrates problems and methods of data mining, detection of patterns, sequences and association rule mining, basic techniques of text mining and clustering methods. Introduction to chaos theory and fractals, utlization in geoinformatics. Stochastic spatial simulations.

Compulsory literature:

AWANGE, J.M., PALÁNCZ, B., LEWIS, R.H., VOLGYESI, L. Mathematical geosciences. Springer Berlin Heidelberg, New York, NY, 2017. BRAMER, M.A. Principles of data mining. Springer, London, 2020. KANEVSKI M. F., Poudnoukhov A., Timonin V. Machine learning for spatial environmental data. CRC Press 2009. 377 s., 978-0-8493-8237-6 ZAKI, M.J., MEIRA, W. Data mining and machine learning: fundamental concepts and algorithms. Cambridge University Press, Cambridge, United Kingdom, 2020; New York, NY.

Recommended literature:

BRUNTON, S.L., KUTZ, J.N. Data-driven science and engineering: machine learning, dynamical systems, and control. Cambridge University Press, Cambridge, 2019. DAUPHINÉ, André. Fractal Geography. Wiley, 2012. ISBN 978-1-84821-328-9. KANEVSKI M. F. Advanced mapping of environmental data : geostatistics, machine learning and Bayesian maximum entropy. ISTE 2008. 313 s., 978-1-84821-060-8 MILLER H. J., HAN J. Geographic Data Mining and Knowledge Discovery. Chapman & Hall/CRC, 2009.

Way of continuous check of knowledge in the course of semester

Students are asked about knowledge from areas that they should have already known from previous lectures. Students also work on individual tasks. Tasks are frequently based on understanding of previous, simpler tasks.


Other requirements

No additional requirements are imposed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1) Artificial intelligence, basic aproaches, methods. 2) Machine learning, basic concepts, supervised learning, unsupervised learning, backpropagation, hybrid methods. Review of machine learning tasks. Model complexity, loss function, dimenzionality. 3) Spatial aspects – spatial constinuity, stacionarity, spatial sampling, bootstrapping. Preparation of analysis– analysis of sample network, Morisita diagram, data transformation. 4) Introduction to classification. Naive Bayes classification. K-means neighbors algorithm. 5) Decision trees. Selection of attributes using entropy, frequency tables, Gini index. Evaluation of classification accuracy. 6) Support vector machines, regression with SVM (SVR). 7) Neural networks, multilayer perceptron, regression neural networks, probable neural networks, Kohonen maps, radial function, deep learing, convolutional neural network. 8) Data mining, data science. Data mining methodology. Pattern mining, sequences. Association rules learning.. 9) Text mining. Text preprocessing. Information lift. Weight normalisation. 10) Cluster analysis, hierarchical and nonhierarchical clustering, association rules, density clusters 11) Introduction to chaos and fractals theories. Model dynamics and dynamic basics. Chaos detection in geography. 12) Fractals. Fractal dimension and its estimation using selected algorithms. Application in geoinformatics 13) Introduction to genetic programming. Sworm intelligence.

Conditions for subject completion

Full-time form (validity from: 2021/2022 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 33  17
        Examination Examination 67 (67) 18 3
                písemná zkouška Written examination 50  18
                ústní zkouška Oral examination 17  0
Mandatory attendence participation: Continuous check of processing tasks during exercises. Written and oral examination.

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Conditions for subject completion and attendance at the exercises within ISP: Materials for an individual study are available at http://homel.vsb.cz/~hor10/Vyuka/ where you can find also topics for the exam. Consultations (both personal and online) with the lecturer are possible. The exercises are individual based on a semester project which has to be completed to the end of the exam period for the given semester. The exam is conducted only in person.

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0532A330044) Geoinformatics AGI P English Ostrava 2 Compulsory study plan
2023/2024 (N0532A330044) Geoinformatics AGI P English Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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