Earthquake Reconnaissance Data Sources, a Literature Review
Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting data on building damage and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guid...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Earth |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4834/2/4/60 |
| _version_ | 1827672996354981888 |
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| author | Diana Contreras Sean Wilkinson Philip James |
| author_facet | Diana Contreras Sean Wilkinson Philip James |
| author_sort | Diana Contreras |
| collection | DOAJ |
| description | Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting data on building damage and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 39 articles that indicate the sources used by different authors to collect data related to damage 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 depending on what questions are to be answered by these data. We conclude that modern reconnaissance missions cannot rely on a single data source. Different data sources should complement each other, validate collected data or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important data source. |
| first_indexed | 2024-03-10T04:16:46Z |
| format | Article |
| id | doaj.art-36fb44dca011467fa24867dd9b51b97a |
| institution | Directory Open Access Journal |
| issn | 2673-4834 |
| language | English |
| last_indexed | 2024-03-10T04:16:46Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Earth |
| spelling | doaj.art-36fb44dca011467fa24867dd9b51b97a2023-11-23T07:58:06ZengMDPI AGEarth2673-48342021-11-01241006103710.3390/earth2040060Earthquake Reconnaissance Data Sources, a Literature ReviewDiana Contreras0Sean Wilkinson1Philip James2Main Building, School of Earth and Environmental Sciences, College of Physical Sciences and Engineering, Cardiff University, Park Place, Cardiff CF10 3AT, UKDrummond Building, School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKUrban Sciences Building, Newcastle Helix, School of Engineering, Newcastle University, Newcastle upon Tyne NE4 5TG, UKEarthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting data on building damage and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 39 articles that indicate the sources used by different authors to collect data related to damage 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 depending on what questions are to be answered by these data. We conclude that modern reconnaissance missions cannot rely on a single data source. Different data sources should complement each other, validate collected data or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important data source.https://www.mdpi.com/2673-4834/2/4/60earthquake reconnaissancefieldwork surveysclosed-circuit television videos (CCTV)remote sensing (RS)crowdsourcing platformssocial media (SM) |
| spellingShingle | Diana Contreras Sean Wilkinson Philip James Earthquake Reconnaissance Data Sources, a Literature Review Earth earthquake reconnaissance fieldwork surveys closed-circuit television videos (CCTV) remote sensing (RS) crowdsourcing platforms social media (SM) |
| title | Earthquake Reconnaissance Data Sources, a Literature Review |
| title_full | Earthquake Reconnaissance Data Sources, a Literature Review |
| title_fullStr | Earthquake Reconnaissance Data Sources, a Literature Review |
| title_full_unstemmed | Earthquake Reconnaissance Data Sources, a Literature Review |
| title_short | Earthquake Reconnaissance Data Sources, a Literature Review |
| title_sort | earthquake reconnaissance data sources a literature review |
| topic | earthquake reconnaissance fieldwork surveys closed-circuit television videos (CCTV) remote sensing (RS) crowdsourcing platforms social media (SM) |
| url | https://www.mdpi.com/2673-4834/2/4/60 |
| work_keys_str_mv | AT dianacontreras earthquakereconnaissancedatasourcesaliteraturereview AT seanwilkinson earthquakereconnaissancedatasourcesaliteraturereview AT philipjames earthquakereconnaissancedatasourcesaliteraturereview |