The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W15
https://doi.org/10.5194/isprs-archives-XLII-2-W15-921-2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-921-2019
23 Aug 2019
 | 23 Aug 2019

PHOTOGRAMMETRY AS A PARTICIPATORY RECOVERY TOOL AFTER DISASTERS: A GROUNDED FRAMEWORK FOR FUTURE GUIDELINES

C. Pezzica, A. Piemonte, C. Bleil de Souza, and V. Cutini

Keywords: Collaborative Mapping, Street-level data, Photogrammetry, Disaster Recovery, Guidelines, Crowdsourcing, SfM

Abstract. This paper identifies the application domain, context of use, processes and goals of low-cost street-level photogrammetry after urban disasters. The proposal seeks a synergy between top-down and bottom-up initiatives carried out by different actors during the humanitarian response phase in data scarce contexts. By focusing on the self-organisation capacities of local people, this paper suggests using collaborative photogrammetry to empower communities hit by disasters and foster their active participation in recovery and reconstruction planning. It shows that this task may prove technically challenging depending on the specifics of the collected imagery and develops a grounded framework to produce user-centred image acquisition guidelines and fit-for-purpose photogrammetric reconstruction workflows, useful in future post-disaster scenarios. To this end, it presents an in-depth analysis of a collaborative photographic mapping initiative undergone by a group of citizen-scientists after the 2016 Central Italy earthquake, followed by the explorative processing of some sample datasets. Specifically, the paper firstly presents a visual ethnographic study of the photographic material uploaded by participants from September 2016 to November 2018 in the two Italian municipalities of Arquata del Tronto and Norcia. Secondly, it illustrates from a technical point of view issues concerning the processing of crowdsourced data (e.g. image filtering, selection, quality, semantic content and 3D model scaling) and discusses the viability of using it to enrich the pool of geo-information available to stakeholders and decision-makers. Final considerations are discussed as part of a grounded framework for future guidelines tailored to multiple goals and data processing scenarios.