Abstract
An optimized planning and realization of the survey, coupled with well thought-out processing, allows obtaining good quality results, while guaranteeing a reasonable use of resources and time. It represents a benefit for both operators and end-users. The former can save time and acquire smaller datasets to process, while the latter can invest their resources better. These goals are even more important in case of an emergency, because the circumstances can quickly change, causing risk to both people and goods. The paper examines the possibility of using unmanned aerial systems (UAS) photogrammetry for 3D modeling in such scenario, focusing on finding a compromise between the final accuracy and the requested processing time. An experimental test has been conducted over the Castle of Casalbagliano, a damaged structure located near Alessandria (Piedmont, Italy), simulating a post-emergency scenario. Several processing strategies have been tested to define a workflow useful in this kind of situations. The quality of the different processing has been evaluated in terms of both residuals of the bundle block adjustment and quality of the generated dense point cloud, compared with a reference terrestrial laser scanner acquisition. Finally, the possibility of publishing the obtained 3D models on the web has been exploited too.
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Acknowledgements
This work comes from the master thesis of one of the authors. The authors would like to thank for the provided support of the following:
• Comune di Alessandria, in particular Arch. Marco Genovese and Geom. Gianfranco Ferraris, for their availability;
• Soprintendenza belle arti e paesaggio della Provincia di Alessandria, in particular Arch. Luigi Pedrini and Dott. Valentina Uras;
• the co-supervisors of the thesis: Eng. Serena Cattari and Arch. Rita Vecchiattini, respectively, at DICCA and DAD departments at the Genoa University.
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Gagliolo, S., Fagandini, R., Passoni, D. et al. Parameter optimization for creating reliable photogrammetric models in emergency scenarios. Appl Geomat 10, 501–514 (2018). https://doi.org/10.1007/s12518-018-0224-4
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DOI: https://doi.org/10.1007/s12518-018-0224-4