Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Earthquakes Reconnaissance Data Sources, a Literature Review

Version 1 : Received: 28 June 2021 / Approved: 29 June 2021 / Online: 29 June 2021 (22:42:45 CEST)

How to cite: Contreras Mojica, D.M.; Wilkinson, S.; James, P. Earthquakes Reconnaissance Data Sources, a Literature Review. Preprints 2021, 2021060714 (doi: 10.20944/preprints202106.0714.v1). Contreras Mojica, D.M.; Wilkinson, S.; James, P. Earthquakes Reconnaissance Data Sources, a Literature Review. Preprints 2021, 2021060714 (doi: 10.20944/preprints202106.0714.v1).

Abstract

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and guide more efficient data. This paper reviews 38 articles that indicate the sources used by different authors to collect data related to damages and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria. Nowadays, reconnaissance mission can not rely on a single data source, and different data sources should complement each other, validate collected data, or quantify the damage comprehensively. The recent increase in the number of crowdsourcing and SM platforms as a source of data for earthquake reconnaissance is a clear indication of the tendency of data sources in the future.

Subject Areas

Earthquake reconnaissance; damage assessment; data sources; data collection; fieldwork surveys; closed-circuit television videos (CCTV); remote sensing (RS); crowdsourcing platforms; social media (SM)

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.