Environmental Engineering

General outcomes The course finds its motivation in the wide and continuously increasing availability of Earth Observation data, acquired by a variety of satellite missions. A large part of these remote sensing data comes from public programs (e.g. Copernicus from EU, Landsat from US), and it is made available for free on dedicated cloud-based platforms for planetary-scale environmental data analysis (e.g. Google Earth Engine, ESA DIAS). In addition, another large amount of data can be collected on the ground by different widely common low-cost sensors (e.g. those embedded in smartphones) through Volunteered Geographic Information (VGI) and crowdsourcing; these ground data are generally linked to a position using GPS or similar Global Navigation Satellite Systems (GNSS: Galileo, GLONASS, Beidou). Both these kinds of remote sensing and ground data are therefore geospatial “big” data, due to their “4V” (Volume, Variety, Velocity, Veracity) features. They can be integrated in between, and with other already available geospatial information, and represent an unprecedented resource to monitor the status and change of our planet in several respects (e.g. climate change effects, SDGs achievement), useful to scientists, technicians and decision makers. The course aims to provide the fundamentals on the main methodologies and techniques currently available for remote sensing and ground geospatial (big) data acquisition, verification, analysis, storage and sharing, also considering that the vast majority (a percentage close to 80%) of the currently available data is geospatial. Knowledge and understanding Students who have passed the exam will know the fundamentals on the main methodologies and techniques currently available for geospatial data acquisition, verification, analysis, storage and sharing, with focus on reference frames and reference systems on the Earth, fundamentals of cartography, photogrammetry and remote sensing, GNSS remote sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant resources represented by Volunteered Geographic Information (VGI) and crowdsourcing. Applying knowledge and understanding Students who have passed the exam will be able to plan and manage the acquisition, verification, analysis, storage and sharing of geospatial data necessary to solve interdisciplinary problems, using GNSS, photogrammetry and remote sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant additional contributions which can be supplied by Volunteered Geographic Information (VGI) and crowdsourcing Making judgment Students will acquire autonomy of judgment thanks to the skills developed during the execution of the numerical and practical exercises that will be proposed on the main topics of the course photogrammetry and remote sensing, Google Earth Engine) Learning skills The acquisition of basic methodological skills on the topics covered, together with state-of-the-art operational skills, favors the development of autonomous learning skills by the student, allowing continuous, autonomous and thorough updating