Abstract
Abstract. During a disaster, the activity of the crowd represents a very valuable source of the on-the-ground conditions shared by the affected citizens. The approach, presented in the paper, explores the relationship between the spatial distribution of crowdsourced image posts and damaged buildings in order to understand the potential of modelling the spatial distribution of damaged buildings based on geolocated images. The posts related to the hurricane Michael that happened in the United States in October 2018, showing the building damage of Panama City, have been collected by NAPSG Foundation and GISCorps volunteers. The building damage assessment, based on the analysis of high-resolution post-event imagery, has been performed by FEMA. Exploring the two available independent point datasets, the spatial pattern of each individual dataset has been analysed and furthermore the spatial relationship between them has been explored. A set of spatial statistics has been performed with R software. For this purpose, the distance-based methods have been used, that consider the mutual position of points to describe the patterns. The results shown the spatial relationship between the crowdsourced photos and different damage types. Furthermore, potential of crowdsourced images for improving the awareness of the structural damage after the hurricane have been discussed.
Highlights
Mobile technologies, web-based platforms, and social media have made it possible to exchange information related to any topic, including natural and human driven disasters
The work presented in this paper explores the relationship between geolocated crowdsourced photos of damaged buildings and building damage obtained from remote sensing data
The aim of this work is to explore spatial point processes in order to understand the possibility of crowdsourced data to predict an early estimate of the structural damage patterns following a hurricane
Summary
Web-based platforms, and social media have made it possible to exchange information related to any topic, including natural and human driven disasters. According to the research of Roberts and Doyle (2017), during a disaster, the crowd engagement is constantly growing. It could represent a very valuable source of the on-the-ground conditions shared by the affected citizens. If this type of source is considered as real-time crowdsourcing of crisis information, the spatial distribution of geolocated images related to an event could represent an early indicator of the severity of its impact (Spasenovic et al, 2019). In the light of previous consideration, the following question may arise: would it be possible to estimate the building damage distribution by exploiting crowdsourced information?
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