MSc στη Γεωπληροφορική και Παρατήρηση Γης

Applications are accepted for admission and commencement of studies in September 2026

Application Deadline: April 30, 2026

MSc in Geoinformatics and Earth Observation (offered in Greek)

Structure and Content

*Under evaluation from the Cyprus Agency of Quality Assurance and Accreditation in Higher Education

Geoinformatics and Remote Sensing synthesize aspects of various scientific fields, as well as associated methods and techniques, including Geodesy, Geophysics, Geographic Information Systems, Photogrammetry, Information Science, Geomathematics, Geostatistics, which are employed in all stages of collection, storage, management, analysis and dissemination of geographic information. Geoinformatics support a wide range of social and environmental decisions, both in the private and public sectors. There is, however, a continuous need for training and specialization of scientists and engineers capable of addressing the increasing requirements of society related to collecting, managing and analyzing geospatial information, the volume of which increases with unprecedented pace.

Earth observation is the gathering of information about planet Earth’s physical, chemical and biological systems. It involves monitoring and assessing the status of, and changes in, the natural and man-made environment. In recent years, Earth observation has become more and more sophisticated with the development of remote-sensing satellites and increasingly high-tech “in-situ” instruments. The integration of novel Earth Observation (EO), space and ground-based integrated technologies, can contribute to a more sustainable and systematic monitoring of the environment, the timely detection of societal risks/threats and the growth of vital economic sectors. The ultimate goal is to foster the sustainable development in line with the international policy framework (EU Societal Challenges, UN SDGs, Sendai Framework, Paris Agreement) and provide critical information through end user products to policy makers, local, national and regional authorities, citizens and tourists.

The master’s program “Geoinformatics and Earth Observation” constitutes a competitive program of international standards, which:

provides the opportunity for specialization and knowledge expansion in Geoinformatics, cutting edge Geospatial Technologies and Earth Observation
promotes high-quality research in the fields of Geoinformatics, Geospatial Technologies and Earth Observation
provides the opportunity to come across with high-level and high-quality research funded activities such as the EXCELSIOR H2020 TEAMING Project
prepares students towards the continuation of their education at a PhD level
equips students with additional qualifications and skills related to Geoinformatics, Geospatial Technologies and Earth Observation in order to seek employment with greater competence in the public or private sector.
provides the opportunity to employees already working in the public or private sector to continue their education in a graduate level, thus offering professional advancement opportunities.

The program consists of 13 courses, which also include a master’s thesis. The course list and content description are given at the tab “Courses”. It is noted that all classes are three (3) hours long and correspond to 6 ECTS, while the master’s thesis corresponds to 30 ECTS.

The students have the opportunity to specialized in two areas: Geoinformatics & Earth Observation (EO). Specialization is achieved via courses in research methods, elective courses, seminars/labs (workshops) and the master’s thesis. Specialization in Geoinformatics aims at providing the methodological and algorithmic/computational knowledge necessary to comprehend relevant new technologies & aims at the application of the above methodologies/technologies in various fields, such as environment, infrastructure, etc. Specialization in EO for Monitoring the Environment aims at providing methodological and algorithmic/computational knowledge necessary to comprehend relevant new technologies of EO in the following areas: Environment and Climate (atmosphere, agriculture, water, land), Resilient Societies (disasters risk reduction, cultural heritage, marine safety and security, energy) and Big Earth Data Management (information extraction, visual exploration and visualization, crowdsourcing and data fusion, Geo-informatics). Emphasis will be given on research excellence in five application areas, which include Climate Change Monitoring, Water Resource Management, Disaster Risk Reduction, Access to Energy and Big EO Data Analytics.

The curriculum of the program includes only compulsory courses, which are credited with six (6) ECTS each. Five (5) courses are held during the first semester (Autumn semester) and five (5) during the second semester (Spring semester). The topic of the postgraduate dissertation is chosen within the autumn semester and is inextricably linked to the course of research methods and specialization. The elaboration of the postgraduate dissertation starts at the beginning of the summer period, once the teaching part of the program is successfully completed, and is expected to be completed at the end of the summer period.

The program is offered in the form of full or part time. In the case of full-time study, the program is completed in thirteen (13) months which includes two academic semesters and the summer period. The latter begins immediately after the end of the Spring semester and includes the months of June, July, August and September. In the case of part-time study, the program can be completed in four (4) academic semesters. For the summer period after the end of the two years of teaching, the same applies in the case of full-time study.

Admission

*Under evaluation from the Cyprus Agency of Quality Assurance and Accreditation in Higher Education

Holders of a degree from a recognized university, or holders of a degree that has been deemed equivalent to a university degree by the Cyprus Degree Evaluation Council (KY.S.A.T.S.), have the right to apply for the Master's program. Undergraduate students, who are expected to receive a university degree before the start of the postgraduate program, can also apply.

Candidates are informed of the outcome of their application in the e-mail they have stated when submitting their application and through the University Portal.

Advance payment is a condition for securing a position that has been offered. This amount is not refundable in any case. See here the amount of the deposit and how to repay the tuition.
Applications together with the supporting documents are submitted electronically, through the University Portal. Create an application here.

Applicants must submit the following electronically in order to apply:

Photocopy of political identity card or passport.
Curriculum vitae
Photocopies of university degrees (or a certificate confirming that the university degree will be obtained before the start of the postgraduate program).
Detailed degree rating
Short report (approximately 500 words), in which the candidate explains the reasons for choosing the postgraduate program and describes his / her academic and research interests, in relation to his / her future professional plans.
Letters of Recommendation are not required. It is only necessary to state two names / details / telephone numbers of the persons who can report and the Department, if it deems it, will contact them directly.
Certificate of proficiency in the Greek language: Acceptable evidence of proficiency in the Greek language are considered the diploma of a recognized six-grade Secondary School of Greece or Cyprus or Lyceum that has Greek as the main language of instruction.

The Department may request additional confidential information from the candidate as well as adopt any additional criteria it deems necessary.

Courses

*Under evaluation from the Cyprus Agency of Quality Assurance and Accreditation in Higher Education

1st semester

GEO 551	Compulsory	Geographic Information Systems (GIS) and Science
GEO 552	Compulsory	Geospatial Data Acquisition
GEO 553	Compulsory	Remote Sensing and Earth Observation
GEO 554	Compulsory	Digital Imaging, Photogrammetry & Computer Vision
GEO 555	Course of specialization	Research Methods: Geoinformatics & Earth Observation

2nd semester

GEO 561	Compulsory	Geospatial Data Science
GEO 562	Compulsory	Earth Observation for Environmental Monitoring
GEO 563	Compulsory	Space-based Positioning and Deformation Monitoring Techniques
GEO 564	Elective	Special Topics in GIS
GEO 565	Elective	Special Topics in Earth Observation
GEO 566	Elective	Special Topics in Earth Data Analytics
GEO 567	Course of specialization	Specialization: Geoinformatics & Earth Observation

Course Description

Course Title	Geographic Information Systems (GIS)
Course Code	GEO 551
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 1st
Teacher’s Name	Phaedon Kyriakidis (Coordinator)
ECTS	6	Lectures / week		1 / 3 hours /week	Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	Presentation of methods and techniques of Geoinformatics and Geospatial Technologies applications via Geographic Information Systems. To create appropriate geovisualizations for the representation of data and analysis results within a GIS environment.
Learning Outcomes	Upon successful completion of the course, students will be able to: Develop geodatabases within a GIS environment. Analyze spatial data using GIS tools. Design effective geovisualizations for presenting data and analytical results in a GIS. More specifically, upon completion of the course, the student will be able to: Understand the fundamental concepts of Geoinformatics, the role of GIS, and its relationship with modern spatial technologies (UAVs, WebGIS, Remote Sensing, BIM, etc.). Organize and manage geospatial data from multiple sources (UAVs, GNSS, open data repositories, IoT sensors). Locate and evaluate available online datasets by assessing their quality (metric accuracy, metadata, ISO & OGC standards). Distinguish and apply appropriate spatial data representation methods (vector and raster) depending on the application requirements. Apply geoprocessing methods (buffering, overlay, network analysis, spatial interpolation) to support informed and evidence-based decision-making. Use GIS tools to produce maps and geovisualizations applying proper cartographic design principles, symbology, and visual hierarchy. Develop WebGIS applications and dashboards and present dynamic spatial information online. Collaborate in project-based environments, implementing methodologies for data collection, processing, analysis, and presentation of results.
Prerequisites	None		Required
Course Content	The course “Geoinformatics and Geographic Information Systems (GIS)” introduces students to the fundamental concepts, methodologies, and technologies related to the collection, organization, analysis, and geovisualization of spatial data. It covers different types of spatial information (vector and raster), reference systems, and methods of data acquisition using UAVs, GNSS, and open geospatial data sources. Special emphasis is placed on spatial processing and analysis techniques such as buffering, overlay, interpolation, and surface analysis (DEM, slope, visibility). In parallel, the course focuses on the principles of proper cartographic design and geovisualization through thematic maps, heatmaps, and spatial dashboards, with the aim of effectively communicating spatial information. Finally, students are trained in developing and publishing WebGIS applications on platforms such as ArcGIS Online, gaining comprehensive practical skills applicable to real-world scenarios. .
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	Bolstad, P. (2012): GIS Fundamentals: A First Text on Geographic Information Systems, 4th Edition, XanEdu Publishing. - Chang, K.-T. (2015): Introduction to Geographic Information Systems, 8th Edition, McGraw Hill. - Harder, C. (2015): The ArcGIS Book: 10 Big Ideas about Applying Geography to Your World, ESRI Press. - Heywood, I., Cornelius, S. and Carver, S. (2012): An Introduction to Geographical Information Systems, 4th Edition, Prentice Hall. - Longley, P., Goodchild, M.F., Maguire, D.J., and Rhind, D.W. (2005) Geographical Information Systems and Science, 2nd Edition, John Wiley & Sons, Chichester, UK. - Mitchel, L., and Collins, A. (2015): Getting to Know ArcGIS for Desktop, Fourth Edition, ESRI Press. - Κουτσόπουλος, Κ., Ανδρουλακάκης, Ν. (2011): Γεωγραφικά Συστήματα Πληροφοριών: Θεωρία και Πράξη, Εκδόσεις Παπασωτηρίου. - Κουτσόπουλος, Κ. (2002): Γεωγραφικά Συστήματα Πληροφοριών και Ανάλυση Χώρου, Εκδόσεις Παπασωτηρίου. - Στεφανάκης, 2010, Βάσεις Γεωγραφικών Δεδομένων και Συστήματα Γεωγραφικών Πληροφοριών. Εκδόσεις Παπασωτηρίου.
Assessment	50% (Five projects / lab exercises) 50% Final written exam
Language	Greek and English

Course Title	Geospatial data acquisition
Course Code	GEO 552
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 1st
Teacher’s Name	Christodoulos Danezis (Coordinator)
ECTS	6	Lectures / week	1 / 3 hours /week		Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	Purpose: Introduction to advanced geospatial data acquisition via terrestrial, airborne, and space-based methodologies, referencing and management of geoinformation. Objectives: Student acquaintance and understanding of geospatial data acquisition, coordinate reference systems and frames, and management of geoinformation via programming techniques.
Learning Outcomes	Upon successful completion of this course, it is expected that the learner will be able to: 1. Recognize and discriminate terrestrial, airborne and satellite data acquisition methodologies. 2. Classify and appraise national, regional and terrestrial coordinate reference systems (CRS) and transformation processes. 3. Organize heterogeneous geospatial information by means of high-level programming languages, such as Python.
Prerequisites	None			Required
Course Content	Terrestrial, Airborne, and Space-based geospatial data acquisition systems. Introduction to Global Navigation Satellite Systems (GNSS). Basic Principles of Terrestrial, Airborne and Space-based data acquisition. Categories and Types of Geoinformation. Coordinate reference systems, Datums and Map Projections. Coordinate Conversions and Transformations. Data Structures and Geo-information Management. European and National Policies on Geospatial Information Management. Geodata Management and Analysis using programming tools.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	El-Rabbany, Ahmed (2006): Introduction to GPS: The Global Positioning System. Second Edition. Artech House (The GNSS Technology and Applications Series). Kaplan, Elliott; Hegarty, Christopher (2005): Understanding GPS: Principles and Applications, Second Edition. Artech House. Iliffe, Jonathan; Lott, Roger (2008): Datums and Map Projections: For Remote Sensing, GIS and Surveying. Whittles Pub. Meyer, Thomas Henry (2010): Introduction to Geometrical and Physical Geodesy: Foundations of Geomatics. ESRI Press. Shan, Jie; Toth, Charles K. (2008): Topographic Laser Ranging and Scanning: Principles and Processing. CRC Press. Lemmens, Mathias (2011): Geo-information: Technologies, Applications and the Environment. Springer Science & Business Media. El-Sheimy, Naser; Valeo, Caterina; Habib, Ayman (2005): Digital Terrain Modeling: Acquisition, Manipulation and Applications. Artech House. Manolopoulos, Yannis; Papadopoulos, Apostolos N.; Vassilakopoulos, Michael Gr (2005): Spatial Databases: Technologies, Techniques and Trends. Idea Group Inc (IGI) Nasser, Hussein (2014): Learning ArcGIS Geodatabases. Packt Publishing Lee, Kent D.; Hubbard, Steve (2015): Data Structures and Algorithms with Python. Springer Shaw, Zed A. (2013): Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code. Addison-Wesley. Westra, Erik (2013): Python Geospatial Development, Second Edition. Packt Publishing Ltd. Zandbergen, Paul A. (2015): Python Scripting for ArcGIS. Esri Press.
Assessment	50% Lab Exercices 50% Final written examination
Language	Greek and English

Course Title	Remote sensing and Earth observation
Course Code	GEO 553
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 1st
Teacher’s Name	Diofantos Hadjimitsis (Coordinator)
ECTS	6	Lectures / week	1 / 3 hours /week		Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	The course aims to introduce students to the principles, techniques, and applications of Remote Sensing and Earth Observation, with emphasis on satellite and aerial data acquisition, as well as the use of geospatial analytical workflows for environmental, spatial, and operational monitoring. Students will develop a theoretical understanding of the physical principles of electromagnetic radiation, sensors and platforms, along with practical skills in remote sensing data processing and analysis. The main objective of the course is for students to gain a solid understanding of the fundamental principles of remote sensing, satellite data processing, and emerging trends in Earth observation.
Learning Outcomes	Upon successful completion of the course, the student will be able to: Understand the fundamental physical principles of Remote Sensing, including the interaction of electromagnetic radiation with matter and the importance of spectral signatures. Identify and compare Earth Observation sensors and platforms. Process and correct satellite and aerial imagery by applying essential procedures such as georeferencing, orthorectification, spectral analysis, and classification. Apply methods for extracting spatial information, including vegetation indices (e.g., NDVI), change detection, and environmental indicator calculations. Use specialized GIS and Remote Sensing software (e.g., ArcGIS, QGIS, SNAP, ENVI) for managing and analyzing remote sensing datasets. Evaluate the suitability of Earth Observation data for real-world applications such as natural resource management, environmental monitoring, disaster assessment, urban development, and climate change analysis.
Prerequisites	None			Required
Course Content	Remote Sensing Principles, role and importance of satellite remote sensing, Earth observation, network of earth observation, Passive and Optical Sensors, Characteristics of satellites and satellite data - modern earth observation missions. Pre-processing of satellite images (Geometric corrections, Radiometric corrections, Atmospheric corrections). Post-processing techniques, Fusion of remote sensing data, sub-pixel applications, Hyperspectral data, multi-scale remote sensing (up-scaling / down-scaling techniques), Advanced classification techniques, spectral signatures, spectral libraries. Image Transformation, Indices, Principal Component Analysis (PCA), Tasseled Cap. Field Spectroscopy. Atmospheric Lidar, Microwave, Thermal Images, satellite radar images (SAR). Importance of using ground-based facilities to support earth observations. Geophysical surveys. Systematic monitoring with global systems. Applications. Trends in satellite remote sensing: The international environment and the European Space Agency.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	Lillesand T., Kiefer R.W., Chipman J. (2015) Remote Sensing and Image Interpretation, 7th Edition, Wiley. Hadjimitsis D.G. (2013) Remote Sensing of Environment - Integrated Approaches. INTECH Campbell J.B, Wynne R.H (2011) Introduction to Remote Sensing, Fifth Edition 5th Edition, The Guilford Press. Jensen J. (2007) Remote Sensing of the Environment: An Earth Resource Perspective (2nd Edition) 2nd Edition, Pearson.
Assessment	40% Projects / lab exercises 60% Final written exam
Language	Greek and English

Course Title	Digital Imaging, Photogrammetry & Computer Vision
Course Code	GEO 554
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Dimitrios Skarlatos (Coordinator)
ECTS	6	Lectures / week	1 / 3 hours /week		Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	Familiarization with basic principles of photogrammetry and computer vision techniques, along with their applications in data gathering using cameras and image processing.
Learning Outcomes	Upon completion of this course, it is expected that the learner will be able to: (1) relate photogrammetric data in a cartographic production process, (2) predict the quality of photogrammetric processes and products, as well as prioritize the influence of variables in the final product quality, and (3) use digital photogrammetric systems
Prerequisites	None			Required
Course Content	Historical background. Platforms and sensors. Active and passive sensors. Mathematical and geometrical principles of computer vision. 3D models, orthophotos and deliverables. Data management and quality control of photogrammetric processing. Equipment and software of photogrammetric production. Aerial, terrestrial, and underwater surveying. Simultaneous Location and Mapping along with mobile mapping data acquisition, principles of Artificial Intelligence in object detection in images. Applications on cultural heritage and constructions.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	1. Close Range Photogrammetry: principles, methods and applications. Luhman, Robson, Kyle, Harley. Whittles publishing, 2006. 2. Robotics, Vision and Control: fundamental algorithms in Matlab. Corke. Springer, 2011 3. Computer Vision, Algorithms and applications. Szeliski. Springer, 2011 4. Machine Vision Algorithms and applications, Steger, Ulrich, Wiedemann. Wiley-VCH, 2008.
Assessment	50% Projects / lab exercises 50% Final written exam
Language	Greek and English

Course Title	Research Methods: Geoinformatics & Earth Observation
Course Code	GEO 555
Course Type	Course of specialization
Level	Postgraduate (MSc)
Year / Semester	1st / 1st
Teacher’s Name	Phaedon Kyriakidis (Coordinator)
ECTS	6	Lectures / week	1 / 3 hours /week		Laboratories / week
Course Purpose and Objectives	The course aims to introduce students to research approaches, methodologies, and practices used in the production of scientific knowledge within the fields of Geoinformatics and Earth Observation. Through theoretical instruction and practical application, the course guides students in formulating research questions, designing experimental workflows, collecting and preprocessing geospatial data, and analyzing and visualizing results using GIS tools. The course also emphasizes the development of scientific writing skills, proper documentation, and research ethics, enabling students to conduct independent scientific work and present well-supported findings in academic and professional settings. The course further includes the selection of a postgraduate thesis topic within Geoinformatics and the critical review of relevant scientific literature.
Learning Outcomes	Upon completion of the course, the student will be able to: Identify and describe the main research approaches in Geoinformatics and Earth Observation (quantitative and qualitative methods). Formulate clear research questions and testable hypotheses that can be examined using geospatial data. Select appropriate Earth Observation datasets (e.g., satellite imagery) based on the requirements of the research topic. Apply techniques for collecting, processing, and analyzing geospatial data using modern GIS and remote sensing software. Implement statistical and spatial analytical methods (e.g., spatial statistics, classification, change detection, modeling). Evaluate accuracy and uncertainty and apply validation and verification procedures to ensure reliability of results. Present research outputs through scientific reports, maps, charts, and visualization tools. Adhere to principles of research ethics, scientific writing, citation practices, and FAIR data management. These outcomes are supported by critical review of Greek and international literature in Geoinformatics and by the analysis of methods for developing research questions and hypotheses.
Prerequisites	None			Required
Course Content	Selection of a subfield of Geoinformatics for the purposes of the specialization and master’s thesis, and relevant literature review under the guidance of one or more members of the Faculty and Instructors. The objective of the literature review is the identification of issue warranting further investigation, and/or the identification of new research trends in the particular subfield. The process of literature review can be enriched via seminars/workshops in relevant subfields.
Teaching Methodology	Lectures, on-line supplements, seminars, application demonstrations, lectures from visiting scientists, one-on-one meetings
Bibliography	Gomarasca, M.A. (2009): Basics of Geomatics, Springer Steinberg, S.L., and Steinberg, S.J. (2015): GIS Research Methods: Incorporating Spatial Perspectives, ERSI Press Bhatta, B. (2013): Research Methods in Remote Sensing, Springer Karimi, H. (2009): Handbook of Research on Geoinformatics, IGI Global Albert, D.P., and Dobbs, R.G. (2013): Emerging Methods and Multidisciplinary Applications in Geospatial Research, IGI Global
Assessment	Special Topic (Project)
Language	Greek and English

Course Title	Geospatial Data Science
Course Code	GEO 561
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Phaedon Kyriakidis (Coordinator)
ECTS	6	Lectures / week		1 / 3 hours /week	Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	Presentation of methods and techniques of geospatial data science and its role in Geoinformatics and Geospatial Technologies
Learning Outcomes	Upon completion of this course, it is expected that the student will be able to: Outline the core methods of different disciplines contributing to spatial data science, Select and employ the most appropriate analytical methods depending on the research question and the type of geospatial data required to address that question Synthesize and present high-quality analytical results involving spatial data
Prerequisites	None		Required
Course Content	The role of (geo)spatial data science in Geoinformatics and Earth Observation. Geospatial data types and spatial analysis objectives for each type. Elements of statistics and their application in spatial analysis. Multivariate statistical methods, regression, clustering, classification, as well as machine learning approaches. Methods for the analysis of spatial distribution of point data, graphs and networks. Geostatistics and spatial interpolation. Problems and models for spatial allocation/siting. Spatial models, cellular automata, agent-based models.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	• Comber, L., Brundson, C. (2021): Geographical Data Science and Spatial Data Analysis. SAGE Publishing, London. • Committee on Strategic Directions for the Geographical Sciences in the Next Decade, US National Research Council (2010) Understanding the Changing Planet: Strategic Directions for the Geographical Sciences, National Academy Press, Washington DC. • Fotheringham, A.S., Rogerson, P.A. (2008) The SAGE Handbook of Spatial Analysis, SAGE Publications, London. • Haining, R. (2003) Spatial Data Analysis: Theory and Practice, Cambridge University Press, Cambridge, UK. • Mitchell, A. (1999) The Esri® Guide to GIS Analysis: Volume 1: Geographic Patterns & Relationships, ESRI Press, Redlands, CA. • Mitchell, A. (2009) The Esri® Guide to GIS Analysis: Volume 2: Spatial Measurements & Statistics, ESRI Press, Redlands, CA. • Mitchell, A. (2012) The Esri® Guide to GIS Analysis: Volume 3: Modeling Suitability, Movement, and Interaction, ESRI Press, Redlands, CA. • O'Sullivan D. and Unwin, D.J. (2010) Geographic Information Analysis, 2nd Edition, John Wiley & Sons, New York. • Rediscovering Geography Committee, US National Research Council (1997) Rediscovering Geography: New Relevance for Science and Society, National Academy Press, Washington DC. • Robinson, G.M. (1998) Methods and Techniques in Human Geography, John Wiley & Sons, Chichester, UK.
Assessment	50% Projects / lab exercise 50% Final written exam
Language	Greek and English

Course Title	Earth Observation for Environmental Monitoring
Course Code	GEO 562
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Athos Agapiou (Coordinator)
ECTS	6	ECTS	6		ECTS		6
Course Purpose and Objectives	Participants will be introduced to principles of earth observation for environmental monitoring, satellite imagining sensors, remote sensing, image processing and the trends for environmental monitoring.
Learning Outcomes	Demonstrate the ability to complete an independent, in-depth, thorough and systematic study related to: Climate Change Monitoring, Water Resource Management, Disaster Risk Reduction, Access to Energy and Big EO Data Analytics Demonstrate the ability to identify, analyse, and critically exploit relevant information, data, and concepts for earth observation for environmental monitoring using state of the art infrastructures. Interpreter and evaluate conclusions from the study, and develop results validated through a sound research methodology. Prioritize and critically assess earth observation sensors and space-based solutions for environmental applications
Prerequisites	None			Prerequisites		GEO 551
Course Content	Classification algorithms for land use analysis. Land use changes and environmental protection. Spatio-temporal changes of landscape. Vegetation indices. Integrated analysis of remote sensing images and thematic maps. Remote sensing applications for built and natural environment. Risk assessment using satellite data. Management of monuments and cultural heritage sites. Detect subsurface archaeological remains through image analysis of archival datasets and fusion with high resolution satellite data.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements,
Bibliography	Prasad S. Thenkabail, John G. Lyon, Alfredo Huete, (2019) Hyperspectral Indices and Image Classifications for Agriculture and Vegetation, ISBN 9781138066038, CRC Press Lillesand T., Kiefer R.W., Chipman J. (2015) Remote Sensing and Image Interpretation, 7th Edition, Wiley. Campbell J.B, Wynne R.H (2011) Introduction to Remote Sensing, Fifth Edition 5th Edition, The Guilford Press. Jensen J. (2007) Remote Sensing of the Environment: An Earth Resource Perspective (2nd Edition) 2nd Edition, Pearson.
Assessment	60% Projects / lab exercises 40% Final written exam
Language	Greek and English

Course Title	Advanced Techniques in Satellite Geodesy and Geohazard Monitoring
Course Code	GEO 563
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Christodoulos Danezis (Coordinator)
ECTS	6	Lectures / week		1 / 3 hours /week	Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	Purpose: To develop an understanding of advanced satellite geodesy techniques used for detecting, analyzing, and monitoring geohazards and natural disasters. The course focuses on GNSS and InSAR technologies and their application through the CyCLOPS Strategic Infrastructure for studying geodynamic processes, deformation, and environmental parameters. Objective: To build knowledge and skills for the use, interpretation, and integration of satellite geodesy data in operational and research frameworks for geohazard monitoring, with emphasis on the synergy between space-based techniques, automated monitoring platforms, and national infrastructures such as CyCLOPS.
Learning Outcomes	Upon completion of the course, students will be able to: Demonstrate an in-depth understanding of the principles and methodologies of GNSS and InSAR techniques. Apply integrated GNSS–InSAR monitoring methods for detecting micromovements and deformation. Analyze GNSS datasets to derive atmospheric parameters. Perform SAR calibration procedures using artificial and electronic corner reflectors of the CyCLOPS infrastructure. Apply terrestrial and aerial scanning techniques within integrated geodetic monitoring systems. Use real datasets from the CyCLOPS infrastructure and develop analytical and visualization workflows. Understand the functionality of the TerraMotion and CyGMS platforms for managing and analyzing geodetic and meteorological information in the context of geohazard monitoring and early warning systems. Participate actively in processes of analysis, modeling, and interpretation of geodetic data within an operational geohazard monitoring framework.
Prerequisites	None		Required
Course Content	Fundamental principles of satellite geodesy and GNSS mathematical models. Positioning errors and modeling/correction techniques in GNSS. Fundamental principles of Synthetic Aperture Radar (SAR). InSAR / DInSAR / PSInSAR / SBAS techniques for deformation detection. Integrated GNSS–InSAR approaches for comprehensive monitoring of ground displacement. Estimation of atmospheric parameters and application of correction models in satellite measurements through GNSS meteorology and in-situ data from the CyCLOPS infrastructure stations. Calibration of SAR imagery using artificial and electronic corner reflectors. Role of terrestrial laser scanning (TLS) and UAVs in 3D mapping of built and natural environments and in geohazard monitoring. The CyCLOPS Strategic Infrastructure: networks, sensors, data infrastructure, and example applications. TerraMotion and CyGMS platforms: architecture, APIs, data flows, visualization, and early warning capabilities. Applications in real case studies: Landslides in urban environments Seismic and tectonic deformation Sea level and coastline monitoring using the integrated LEME tide gauge system of CUT Environmental and atmospheric GNSS applications
Teaching Methodology	Lectures, computer-based laboratory sessions, hands-on exercises using real datasets from the CyCLOPS network, processing and analysis of InSAR using high-resolution imagery, group projects, use of online tools, and invited guest researcher presentations.
Bibliography	El-Rabbany, Ahmed (2006): Introduction to GPS: The Global Positioning System. Second Edition. Artech House (The GNSS Technology and Applications Series). Kaplan, Elliott; Hegarty, Christopher (2005): Understanding GPS: Principles and Applications, Second Edition. Artech House. Iliffe, Jonathan; Lott, Roger (2008): Datums and Map Projections: For Remote Sensing, GIS and Surveying. Whittles Pub. Meyer, Thomas Henry (2010): Introduction to Geometrical and Physical Geodesy: Foundations of Geomatics. ESRI Press. Shan, Jie; Toth, Charles K. (2008): Topographic Laser Ranging and Scanning: Principles and Processing. CRC Press. Lemmens, Mathias (2011): Geo-information: Technologies, Applications and the Environment. Springer Science & Business Media. El-Sheimy, Naser; Valeo, Caterina; Habib, Ayman (2005): Digital Terrain Modeling: Acquisition, Manipulation and Applications. Artech House. Manolopoulos, Yannis; Papadopoulos, Apostolos N.; Vassilakopoulos, Michael Gr (2005): Spatial Databases: Technologies, Techniques and Trends. Idea Group Inc (IGI). Nasser, Hussein (2014): Learning ArcGIS Geodatabases. Packt Publishing Ltd. Lee, Kent D.; Hubbard, Steve (2015): Data Structures and Algorithms with Python. Springer. Shaw, Zed A. (2013): Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code. Addison-Wesley. Westra, Erik (2013): Python Geospatial Development, Second Edition. Packt Publishing Ltd. Zandbergen, Paul A. (2015): Python Scripting for ArcGIS. Esri Press.
Assessment	50% Lab Exercises – 50% Final Examination
Language	Greek and English

Course Title	Special Topics in GIS & Geovisualization
Course Code	GEO 564
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Apostolos Papakonstantinou (Coordinator)
ECTS	6	Lectures / week	1 / 3 hours /week		Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	Specialization in Geoinformatics and Geovisualization of advanced applications of Geographic Information Systems and Science. Aims to provide students with the theoretical foundation and technical skills to design, implement, and evaluate modern geovisualization systems that support spatial analysis, decision-making, and public communication of geospatial data.
Learning Outcomes	Upon completion of this course, it is expected that the learner will be able to: Collect, manage and analyze geospatial data for a variety of applications Evaluate state-of-the-art methods and technologies in Geographic Information Systems and Science for selected application domains Understanding the principles and methods of advanced spatial data visualization. Explain the theoretical foundations and evolution of GIS-based geovisualization. Apply appropriate cartographic and visualization techniques for representing multidimensional spatial data. Integrate various data sources (satellite, UAV, LiDAR, crowdsourced) into interactive GIS environments. Design and implement web maps and 3D/VR visualizations using modern geospatial platforms.
Prerequisites	None			Required
Course Content	Use of GIS (User Needs, Provision of Information, Decision Support Systems, Infrastructure, Legal Framework). Applications of GIS. Database organization and selection of a GIS depending upon a specific application. Application of GIS in specific fields. GIS applications in Cyprus. Applications in various fields : Environment, water quality, air pollution, agriculture, geology, blue growth, social policy, urban planning, land register, coastal, project management, road - management and road works, telecommunications, archeology, cultural heritage, etc. New methods of acquiring and analyzing geospatial data. Geovisualization: Principles and methods for visual representation of spatial information. Use of 2D, 3D, and immersive visualization techniques to enhance spatial understanding and decision support. Integration of real-time and multidimensional data in interactive visual environments. Design of effective visual storytelling for communication of geospatial analyses and results.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	- Bolstad, P. (2012): GIS Fundamentals: A First Text on Geographic Information Systems, 4th Edition, XanEdu Publishing. - Chang, K.-T. (2015): Introduction to Geographic Information Systems, 8th Edition, McGraw Hill. - Harder, C. (2015): The ArcGIS Book: 10 Big Ideas about Applying Geography to Your World, ESRI Press. - Heywood, I., Cornelius, S. and Carver, S. (2012): An Introduction to Geographical Information Systems, 4th Edition, Prentice Hall. - Mitchel, L., and Collins, A. (2015): Getting to Know ArcGIS for Desktop, Fourth Edition, ESRI Press. - Mitchell, A. (1999) The Esri® Guide to GIS Analysis: Volume 1: Geographic Patterns & Relationships, ESRI Press, Redlands, CA. - Mitchell, A. (2009) The Esri® Guide to GIS Analysis: Volume 2: Spatial Measurements & Statistics, ESRI Press, Redlands, CA. - Mitchell, A. (2012) The Esri® Guide to GIS Analysis: Volume 3: Modeling Suitability, Movement, and Interaction, ESRI Press.
Assessment	In class exercises 50% 50% Special Topic (Project)
Language	Greek and English

Course Title	Special Topics in Earth Observation
Course Code	GEO 565
Course Type	Elective
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Diofantos Hadjimitsis (Coordinator)
ECTS	6	Lectures / week	1 / 3 hours /week		Laboratories / week	Included in the 3 hours (upon schedule)
Course Purpose and Objectives	The course “Special Topics in Earth Observation” aims to provide in-depth knowledge of advanced concepts, techniques, and emerging applications in Earth Observation, with emphasis on innovative technologies, research approaches, and modern methods for geospatial data analysis. The course focuses on specialized thematic areas such as multispectral and hyperspectral remote sensing, active sensor systems, satellite time-series analysis, machine learning for remote sensing, cloud-based processing, and open platforms (e.g., Google Earth Engine). Through case studies, recent scientific publications, and applied examples, students gain knowledge and hands-on experience in using Earth Observation data to analyze complex phenomena such as climate change, natural disasters, environmental monitoring, agricultural applications, hydrology, and urban transformation. The course promotes critical thinking, applied data analysis, and the development of advanced research skills, enabling students to leverage Earth Observation data for real-world operational, scientific, and decision-support applications.
Learning Outcomes	Upon completion of the course, the student will be able to: Analyze advanced types of Earth Observation data, such as hyperspectral and thermal imagery. Apply modern analytical approaches, including deep learning, cloud-based processing, and automated classification workflows. Manage large-scale remote sensing datasets (Big Earth Data) using specialized tools and online computational platforms. Develop complete processing workflows for specialized applications (e.g., change detection, biomass estimation, flood or burned area mapping). Identify the capabilities, limitations, and requirements of different remote sensing sensors and analytical approaches. Evaluate the quality and reliability of generated geospatial products and analytical outcomes. Select and use advanced geovisualization tools for documenting and communicating remote sensing analyses. Link Earth Observation outputs to land policy, environmental management, and decision-support contexts.
Prerequisites	None			Required
Course Content	The course also introduces modern methodologies such as artificial intelligence, machine learning, and deep learning techniques for automated classification and information extraction from large-scale remote sensing datasets, as well as cloud-based computational environments including Google Earth Engine, ESA DIAS, and Copernicus Data Spaces. Special emphasis is placed on the management, quality assessment, and validation of remote sensing products, as well as on the development of analytical workflows that integrate Earth Observation data with GIS and geospatial modeling. The course concludes with case studies and the development of a small applied research project tailored to real environmental, societal, or management challenges, with the aim of strengthening skills in critical thinking, analysis, and scientific documentation. Case studies include topics such as: Bathymetry estimation using optical satellite imagery Contribution of remote sensing to Marine Spatial Planning Precision agriculture and evapotranspiration estimation Vegetation recognition and classification Phenological monitoring Urban heat island analysis using satellite observations Monitoring networks Estimation of water losses in agricultural areas, and more.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	Prasad S. Thenkabail, John G. Lyon, Alfredo Huete, Hyperspectral Remote Sensing of Vegetation, ISBN 9781439845370 Campell, J. B. (1996). Introduction to Remote Sensing (2nd edition). London: Taylor & Francis Lillesand, T., Kiefer R. and Chipman J. (2008) Remote Sensing and Image Interpretation, 6th edition, John Wiley, ISBN 978-0-470-05245-7 Jensen J. (2004) Introductory Digital Image Processing – A Remote Sensing Perspective, 3rd edition Prentice Hall, ISBN-10: 0131453610 Satellite Remote Sensing, A New Tool for Archaeology, Editors: Lasaponara, Rosa, Masini, Nicola (Eds.), Springer, ISBN: 978-90-481-8800-0 Jensen J. (2007) Remote Sensing of the Environment – An Earth Resource Perspective, 2nd edition, , Prentice Hall, ISBN-10: 0131889508
Assessment	100% Special Topic (Project)
Language	Greek and English

Course Title	Special Topics in Earth Data Analytics
Course Code	GEO 566
Course Type	Elective
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Phaedon Kyriakidis (Coordinator)
ECTS	6	Lectures / week		1 / 3 hours /week	Laboratories / week
Course Purpose and Objectives	Specialization in Earth Data Analytics and its applications in Geoinformatics and Earth Observation.
Learning Outcomes	Upon successful completion of the course, the student will be able to: Understand in depth advanced geospatial data analysis techniques, including statistical, computational, and algorithmic methods. Perform spatial and temporal analysis, including the use of time series and spatial correlation models. Apply machine learning and artificial intelligence techniques (e.g., Random Forest, SVM, Neural Networks, Deep Learning) for classification, change detection, regression, and spatial prediction. Evaluate accuracy, uncertainty, and quality of results, using modern validation approaches, error metrics, cross-validation, and benchmarking. Integrate analytical outputs into GIS and geostatistical models, generating spatial indicators and decision-support datasets. Address complex scientific or management problems by designing specialized analytical methodologies based on Earth Observation data. Present and document analytical results in a scientific format, through written reports, visualizations, and structured interpretation. Conduct critical evaluation (methodologically and practically) of geospatial data analysis approaches. Select appropriate analytical methods to solve practical geographic and environmental problems.
Prerequisites	None		Required
Course Content	Applications of modern statistical methods for Geoinformatics and Earth Observation. Machine learning for regression, clustering, classification, spatial interpolation and spatial analysis.
Teaching Methodology	Lectures, computer-based lab exercises, on-line training supplements, projects, lectures from visiting scientists
Bibliography	Jiang, Z., Shekhar, S. (2017): Spatial Big Data Science: Classification Techniques for Earth Observation Imagery, Springer. Karimi, H.A. Ed. (2017): Big Data: Techniques and Technologies in Geoinformatics, CRC Press. Lovelace, R., Nowosad, J., Muenchow, J. (2020): Geocomputation with R, CRC Press. • O'Sullivan D. and Unwin, D.J. (2010) Geographic Information Analysis, 2nd Edition, John Wiley & Sons, New York.
Assessment	In class exercises 50% 50% Special Topic (Project)
Language	Greek and English

Course Title	Research Methods II: Geoinformatics & Earth Observation
Course Code	GEO 567
Course Type	Course of specialization
Level	Postgraduate (MSc)
Year / Semester	1st / 2nd
Teacher’s Name	Phaedon Kyriakidis (Coordinator)
ECTS	6	Lectures / week		1 / 3 hours /week	Laboratories / week
Course Purpose and Objectives	Specialization in a subfield of Geoinformatics
Learning Outcomes	Critical review of literature in a subfield of Geoinformatics and/or Earth Observation; development of research questions and hypotheses; use of geospatial technologies in a small-project setting
Prerequisites	None		Required
Course Content	Upon successful completion of the course, the student will be able to: Deepen their knowledge of advanced methodologies in Geoinformatics and Earth Observation, applying specialized data analysis techniques aligned with their selected scientific focus. Design and implement complete research workflows, from defining the research question and methodology to producing and interpreting results. Develop and execute laboratory exercises and analytical procedures using advanced software, platforms, and computational tools related to GIS, remote sensing, and spatial analysis. Apply modern analytical techniques, such as machine learning, spatial statistics, time-series analysis, or predictive modeling, depending on the selected research direction. Critically evaluate research methodologies and outcomes, implementing validation procedures, accuracy assessment, and scientific justification. Stay informed and integrate current developments in the field through participation in seminars, workshops, scientific presentations, and thematic research infrastructures. Collaborate effectively with supervisors and research teams, applying scientific ethics, principles, and standards for data management and dissemination. Produce a high-quality scientific paper or technical report, meeting academic standards of writing, structure, and documentation. Present research results both orally and in writing, using scientific language, maps, visualizations, and appropriate communication deliverables.
Teaching Methodology	Lectures, on-line supplements, seminars, application demonstrations, lectures from visiting scientists, one-on-one meetings
Bibliography	Gomarasca, M.A. (2009): Basics of Geomatics, Springer Steinberg, S.L., and Steinberg, S.J. (2015): GIS Research Methods: Incorporating Spatial Perspectives, ERSI Press, Redlands, CA Bhatta, B. (2013): Research Methods in Remote Sensing, Springer, Karimi, H. (2009): Handbook of Research on Geoinformatics, IGI Global Albert, D.P., and Dobbs, R.G. (2013): Emerging Methods and Multidisciplinary Applications in Geospatial Research, IGI Global
Assessment	Special Topic (Project)
Language	Greek and English

Course Title	Master’s thesis
Course Code	GEO 590
Course Type	Compulsory
Level	Postgraduate (MSc)
Year / Semester	June – September
Teacher’s Name	TEACHING PERSONNEL
ECTS	30	Lectures / week		Laboratories / week
Course Purpose and Objectives	Conducting research (Geoinformatics) or utilizing new technologies (Geospatial Technologies) in application areas related to the environment, infrastructures, cultural heritage, etc.
Learning Outcomes	Conduct applied research and write research report
Prerequisites	Passing grade in all program courses		Required
Course Content	Master’s project and thesis writing under the supervision of one or more program Faculty and Instructors. Widely accepted methodologies and/or research tools should be employed during all stages of the research project. The deliverable should constitute a complete manuscript, and parts of which could be submitted for peer-review and publication in scientific journals. The master’s thesis topic will be selected during the 1st semester, and will be directly linked to the topics of the research methods and specialization courses. It is expected that this practice will promote the continuous student/Faculty/Instructor interaction, as well as lead to a high-quality thesis. The thesis project will commence in June, while the final thesis should be submitted in September.
Teaching Methodology	One-on-one meetings
Bibliography	Friedland, A.J., and Folt, C.L. (2009): Writing Successful Science Proposals, 2nd Ed., Yale University Press, NH Bui, Y. N. (2014): How to Write a Master’s Thesis, 2nd Ed., SAGE Publications
Assessment	Thesis content and quality of presentation
Language	Greek and English