IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Assessment of the Contribution of Crowd Sourced Data to Post-Earthquake Building Damage Detection

Assessment of the Contribution of Crowd Sourced Data to Post-Earthquake Building Damage Detection
View Sample PDF
Author(s): Reza Hassanzadeh (University College Dublin, Ireland)and Zorica Nedovic-Budic (University of Illinois at Chicago, USA & University College Dublin, Ireland)
Copyright: 2019
Pages: 41
Source title: Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8054-6.ch034

Purchase

View Assessment of the Contribution of Crowd Sourced Data to Post-Earthquake Building Damage Detection on the publisher's website for pricing and purchasing information.

Abstract

This paper compares the results of building damage detection based on Crowd Sourced (CS) data, image processing of remotely sensed (RS) data and predictive modelling with institutional spatial data (Spatial Data Infrastructure - SDI). In particular, it focuses on the contribution of Crowd Sourcing to detecting post-earthquake building damages, while also considering the integration of Crowd Sourced with two other data sources (RS and modelling). To simulate CS data submission following the 2003 earthquake in Bam City (Iran) a survey was administered to the population which experienced the earthquake. The results obtained from this and two other sources are compared with the Actual Earthquake (AE) data by cross-tabulation analysis and McNemar's Chi Square Test. When assessed against AE data, the average accuracy levels of assessments based on the use of RS data and CS data integrated with each RS data and predictive modelling and with both, show a statistically significant increase relative to the predictive modelling. While this research does not provide for a full assessment of the value of CS data alone and in fact finds it slightly inferior to predictive modelling, it suggests that Crowd Sourcing could be a useful source of information, especially if combined with other sources.

Related Content

Salwa Saidi, Anis Ghattassi, Samar Zaggouri, Ahmed Ezzine. © 2021. 19 pages.
Mehmet Sevkli, Abdullah S. Karaman, Yusuf Ziya Unal, Muheeb Babajide Kotun. © 2021. 29 pages.
Soumaya Elhosni, Sami Faiz. © 2021. 13 pages.
Symphorien Monsia, Sami Faiz. © 2021. 20 pages.
Sana Rekik. © 2021. 9 pages.
Oumayma Bounouh, Houcine Essid, Imed Riadh Farah. © 2021. 14 pages.
Mustapha Mimouni, Nabil Ben Khatra, Amjed Hadj Tayeb, Sami Faiz. © 2021. 18 pages.
Body Bottom