Catalogo dei Corsi di studio

The vast majority (a percentage close to 80%) of the currently available data has a geographical connotation, is intrinsically linked to a position; they are therefore named geospatial data. Furthermore, the ever-increasing availability of sensors capable of acquiring geospatial data, allowing the acquisition of larger and larger amounts of data, raises several important issues related to the correct, efficient and effective use of these geospatial big data.
This course therefore finds its motivation in the great availability and relevance of geospatial data (in particular big data), and it aims to provide the fundamentals on the main methodologies and techniques currently available for their acquisition, verification, analysis, storage and delivery. Special attention is given to data coming from global navigation satellite systems (GNSS), remote sensing and photogrammetry, volunteered geographic information (VGI) and crowdsourcing, both regarding their analysis and management with freely available software and open source software and their applications.
As overall transversal competences, starting from the specific geomatic methods and techniques, students will be trained to develop skills to design both experiments encompassing geospatial big data acquisition, analysis and interpretation, and to use these data to solve interdisciplinary problems also with original approaches. Moreover, students will get the skills enabling the continuous and autonomous update of their methodological and technical competences during their professional life.

Programme

Geospatial data and geo big data challenges

Geodesy and Cartography:
Spatial and temporal reference systems and frames; coordinate systems; practical exercises with online software

Positioning with Global Positioning System:
positioning and technicalities fundamentals; pseudorange and carrier phase observations modeling and processing; different kinds of GPS surveys, Network Real Time Kinematic surveys; other Global Navigation Satellite Systems; positioning with smartphones; practical exercises with instruments, smartphones and software

Metric information from imagery:
photogrammetry fundamentals and models; 3D object reconstruction (surface modeling); digital imagery main features (geometric, temporal, radiometric and spectral resolution); terrestrial, aerial and satellte imagery processing; automated surface modeling and matching techniques; digital terrain models and orthophotos; practical exercises with instruments, smartphones and software

Geographical Information Science:
data structures, geo big data cloud storage and processing (Google Earth Engine), Volunteered Geographic Information (VGI), crowdsourcing; practical exercises with Google Earth Engine

Adopted texts

Slides of the course


Reference books and articles:
Peter J.G. Teunissen, Oliver Montenbruck (Eds.) (2017). Springer Handbook of Global Navigation Satellite Systems. Springer International Publishing AG. ISBN: 978-3-319-42926-7, DOI 10.1007/978-3-319-42928-1

Karl Kraus (2000). Photogrammetry (vol. 1). Dummler

Zhe Jiang, Shashi Shekhar (2017). Spatial Big Data Science - Classification Techniques for Earth Observation Imagery. Springer International Publishing AG. ISBN 978-3-319-60194-6, DOI 10.1007/978-3-319-60195-3


Reference articles:
Songnian Li, Suzana Dragicevic, Francesc Antón Castro, Monika Sester, Stephan Winter, Arzu Coltekin, Christopher Pettit, Bin Jiang, James Haworth, Alfred Stein, Tao Cheng (2016). Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing 115 (2016) 119–133

Jun Chen, Ian Dowman, Songnian Li, Zhilin Li, Marguerite Madden, Jon Mills, Nicolas Paparoditis, Franz Rottensteiner, Monika Sester, Charles Toth, John Trinder, Christian Heipke (2016). ISPRS Journal of Photogrammetry and Remote Sensing 115 (2016) 3–21

Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, Rebecca Moore (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202 (2017) 18–27

Linda See, Peter Mooney, Giles Foody, Lucy Bastin, Alexis Comber, Jacinto Estima, Steffen Fritz, Norman Kerle, Bin Jiang, Mari Laakso, Hai-Ying Liu, Grega Milˇcinski, Matej Nikšiˇc, Marco Painho, Andrea Podör, Ana-Maria Olteanu-Raimond, Martin Rutzinger (2016). Crowdsourcing, Citizen Science or Volunteered
Geographic Information? The Current State of Crowdsourced Geographic Information. ISPRS Int. J. Geo-Inf. 2016, 5, 55; doi:10.3390/ijgi5050055

Maria Antonia Brovelli, Marco Minghini, Giorgio Zamboni (2016). Public participation in GIS via mobile applications. ISPRS Journal of Photogrammetry and Remote Sensing 114 (2016) 306–315

Prerequisites

No mandatory prerequisites

Exam modes

Written exam with questions about the topics included in the program of the course; evaluation of one report on the practical exercises developed during the course (at student's choice)