The main objective for the students is to understand the importance of the science of remote sensing in management planning. More specifically, it is to implement techniques for satellite and aerial photography data processing in Planning and Regional Development disciplines. For this purpose and in the context of applications, they will learn to use literature through digital libraries. They will also learn to identify and acquire satellite data from different sources, at no cost, correlating them with the appropriate scaling used in Urban and Spatial Planning projects.

The main learning objectives of the undergraduate course are as follows:

• To deepen in techniques and technologies by acquiring specialized and targeted knowledge in software for satellite data and aerial photograph processing (Idrisi, SNAP).
• To understand satellite data and aerial photographs processing methodologies such as: (a) photogrammetric methodologies for thematic information extraction from high resolution aerial photographs, (b) supervised classifications using specialized algorithms, (c) techniques for detecting land cover, vegetation indices etc.
• To be able to apply interdisciplinary approaches to solve spatial problems. For example, biomass estimates using satellite data, field research, statistical analysis etc.

The course is aimed at undergraduate students who have successfully completed the course “Remote Sensing-Photointerpretation” and have acquired the basic knowledge on Remote Sensing.

The course includes a specific case study implementation. In this context, implementations, using specialized methods and applications of Remote Sensing, are applied. Thematically it involves research, primarily in the urban-suburban environment, the natural environment and the rural areas. Methodologically, the following are examined: specific methods of geometric corrections, various classification techniques, temporal change detection methods, vegetation indices, creation of Digital Terrain Models, creation of three-dimensional terrain models, as well as the connection of the results with a Geographic Information System for further processing.

A final project including all topics toughed during the course semester will be demanded by the students and will contribute an 80% of the final grade. The project presentation and applicability will contribute an 20% to the final grade.

• 1. Brandt Tso and Paul M. Mather 2001, ‘Classification methods for remotely sensed data’ Taylor & Francis.
• 2. Paul J. Gibson and Clare H. Power, 2000 ‘Introductory remote sensing: digital image processing and applications’.
• 3. David S. Wilkie and John T. Finn, 1996 ‘Remote sensing imagery for natural resources monitoring: A guide for frist-time users’, Columbia University Press.
• 4. Paul M. Mather, 1989 ‘Computer processing of remotely-sensed images: An introduction’, John Wiley & Sons.
• 5. Περάκης Κ., Φαρασλής Ι., Μωισιάδης Αθ., 2015. «Η Τηλεπισκόπηση σε 13 Ενότητες», Ελληνικά Ακαδημαϊκά Ηλεκτρονικά Συγγράμματα και Βοηθήματα.
• https://repository.kallipos.gr/handle/11419/1840