Course description
The aim of the course is twofold: - First, providing a description of instruments and methods for 3D data acquisition, processing and modelling. - Then, give a general overview of the uses of 3D models and DTMs in real applications.
For each subject a case study taken from a real application will be considered in order to help the students in relating the presented theoretical methods to their real use. In particular, the course aims at presenting the role of 3D modelling and of the considered techniques in certain applications of current wide interests worldwide, e.g. renewable energies, hydro-geological risk and cultural heritage. In the first part of the course, active and passive sensors for 3D data acquisition will be presented, focusing in particular on Laser Scanning (Terrestrial Laser Scanning and Airborne LiDAR) and photogrammetry, and comparing their strengths and weaknesses. Mobile mapping with either terrestrial or aerial vehicles (e.g. drones) will be considered as well. Characteristics of different types of georeferencing methods will be compared. During the second part of the course, techniques for noise removal, filtering, smoothing and interpolation will be presented. Local and global methods for data modelling will be considered. Geostatistics and learning theory play a key role for the selection (and validation) of a good data model and for removing undesired noise while preserving features of interest: the course will provide a presentation of geostatistics methods going from basics of statistics to the use of Kriging in certain cases of interest. Elements of learning theory will also be provided for data model selection. Finally, a list of examples and real applications will be presented, e.g.: monitoring damaged building façades (noise reduction example), 3D data segmentation (e.g. recognition of planar surfaces in buildings), volume computation (e.g. useful in cut and fill problems for road design, site planning, for building dams and reservoirs), watersheds and drainage network estimation, hydrological run-off modelling, painting recognition, thermal data processing (e.g. useful for thermal efficiency evaluation of buildings), classification of fire risk areas, estimating solar irradiance on sites of interest (e.g. planning solar panel distribution). At the end of the course, students will have gained familiarity with spatial data management. They will have learnt characteristics of currently most frequently used techniques for spatial data acquisition. They will have had a good introduction to methods for 3D data processing and manipulation, modelling and analysis. The quite long list of examples and applications, related to subjects of current worldwide interest, presented during the course will be of fundamental importance in order to make them ready to the use of the presented methods in real applications/scenarios. Examples will be shown (and proposed to the students) by using mostly free and / or open source software, hence allowing students have easy application and experimentation of the methods also on their own computers.

Outcomes
Main outcomes of course are:
- Gain familiarity with spatial data management - Learn techniques for spatial data acquisition, processing and manipulation, modelling and analysis. More specifically: a) Data acquisition: use of active (e.g. laser scanning) and passive sensors (e.g. photogrammetry) to acquire spatial data. Students during the course will practice in particular with digital photogrammetric 3D reconstruction, which can be easily obtained by using standard (commercial) digital cameras. b) Data processing and analysis: (statistical) data processing is a fundamental step in dealing with spatial information. Students will learn different techniques for spatial interpolation, noise removal. Thanks to the several real applications of spatial data processing which will be shown during the course students will be able to realize how to apply the considered methods on real data. The possibility of applying these techniques to several fields (as in the case studies of main interest during the course, e.g. renewable energies, hydrogeological risk and cultural heritage) make them particularly interesting for a large number of applications, which will become quite familiar to the students after the course.

Teaching and Evaluation Method
Lessons will be supported by slides and examples. Examples will be of particular importance in order to ease the comprehension of the methods to the students. For each subject several examples will be presented, starting from very simple ones, and arriving to a real case study. Course can be integrated with lab sessions, if possible. Examples will be shown (and proposed to the students) by using mostly free and / or open source softwares, hence allowing students have easy application and experimentation of the methods also on their own computers. Lessons will be given interactively, in particularly during the presentation of examples: students will have the possibility of interacting with the teacher asking questions and they will be asked to think at the possible solutions to the currently examined problem. Lab sessions will be useful to increase the interaction within students (and with the teacher as well).

Evaluation methods
1) 3D modelling and data processing test: 60% of total grade
2) Oral exam: 40% of total grade

Bibliography
Most of the contents of the course are in: - Digital Terrain Modelling. N. El-Sheimy, C. Valeo, A. Habib. Artech House, 2005
A more complete description of certain specific subjects involved in the course can be found in the following books (optional, for those who want to increase their knowledge on these subjects): - Photogrammetric Computer Vision. W. Forstner, B.P. Wrobel. Springer, 2016. - Probability & Statistics for Engineers & Scientists. R.E. Walpole, R.H. Myers, S.L. Myers, K. Ye. Prentice Hall, 2012.
References to scientific publications will be provided during the course for those interested to an in depth learning of such subjects.
Slides used during lessons will be provided to students.