Advertisement

Synergistic Exploitation of Geoinformation Methods for Post-earthquake 3D Mapping and Damage Assessment

  • Nikolaos SoulakellisEmail author
  • Georgios Tataris
  • Ermioni-Eirini Papadopoulou
  • Stamatis Chatzistamatis
  • Christos Vasilakos
  • Dimitris Kavroudakis
  • Olga Roussou
  • Apostolos Papakonstantinou
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

This paper presents a methodological framework, which establishes links among the: i. 3D mapping, ii. 3D model creation and iii. damage classification grades of masonry buildings by European Macroseismic Scale-98 and the application of geoinformation methods towards 3D mapping and damage assessment after a catastrophic earthquake event. We explore the synergistic exploitation of a Real Time Kinematics system, terrestrial photogrammetry, Unmanned Aircraft Systems and terrestrial laser scanner for collecting accurate and high-resolution geospatial information. The proposed workflow was applied at the catastrophic earthquake of June 12th, 2017 on the traditional settlement of Vrisa on the island of Lesvos, Greece. The Structure from Motion method has been applied on the high-resolution terrestrial and aerial photographs, for producing accurate and very detailed 3D point clouds of the damaged buildings of the Vrisa settlement. Additionally, two Orthophoto maps and two Digital Surface Models have been created, with a spatial resolution of 5 cm and 3 cm, respectively. The first orthophoto map has been created just one day after the earthquake, while the second one, a month later. The significant advantages of the proposed methodology are: (a) the production of reliable and accurate 2D and 3D information at both village and building scales, (b) the ability to support scientists during building damage assessment phase and (c) the proposed damage documentation provides all the appropriate information which can augment all experts and stakeholders, national and local organizations focusing on the post-earthquake management and reconstruction processes of the Vrisa traditional village.

Notes

Acknowledgements

This paper is a result of the research project “3D mapping of Vrisa settlement after the 12th June Lesvos earthquake” funded by the North Aegean Region. The authors would like to thank Prof. Pavlogeorgatos G., Chaidas K., Kalaitzis P., Kaloniatis Ch., Doukari M., Drolias A., Mauroeidi A., Zorbas K., Papazis N., Moustakas, A. and Makri D. for supporting the processing stage of this project. In this publication the work related to UAS data acquisition and UAS-SfM process has been carried out within the framework of the Greek State Scholarship Foundation (I.K.Y.) Scholarship Programs funded by the “Strengthening Post-Doctoral Research” Act from the resources of the OP “Human Resources Development and Lifelong Learning “priority axis 6, 8, 9 and co-financed by the European Social Fund–ESF and the Greek government.

References

  1. Adams SM, Friedland CJ (2011) A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management. In: Proceedings of the ninth international workshop on remote sensing for disaster response. Stanford, CA, USA, pp 15–16Google Scholar
  2. Anil EB, Akinci B, Garrett JH, Kurc O (2013) Characterization of laser scanners for detecting cracks for post-earthquake damage inspection. In: 30th ISARC. Montreal, Canada, pp 313–320Google Scholar
  3. Bemis SP, Micklethwaite S, Turner D, James MR, Akciz S, Thiele ST, Bangash HA (2014) Ground-based and UAV-Based photogrammetry: a multi-scale, high-resolution mapping tool for structural geology and paleoseismology. J Struct Geol 69:163–178.  https://doi.org/10.1016/j.jsg.2014.10.007CrossRefGoogle Scholar
  4. Bose S, Nozari A, Mohammadi ME, Stavridis A, Babak M, Wood R, Gillins D, Barbosa A (2016) Structural assessment of a school building in Sankhu, Nepal damaged due to torsional response during the 2015 Gorkha earthquake. In: Pakzad S, Juan C (eds) Conference proceedings of the society for experimental mechanics series, Dynamics of civil structures. Springer, Cham, pp 31–41Google Scholar
  5. Calantropio A, Chiabrando F, Sammartano G, Spanò A, Losè LT (2018) UAV strategies validation and remote sensing data for damage assessment in post-disaster scenarios. Int Arch Photogramm Remote Sens Spat Inf Sci XLII-3/W4:121–128.  https://doi.org/10.5194/isprs-archives-xlii-3-w4-121-2018
  6. Chang KT, Wang EH, Chang YM, Cheng HK (2008) Post-disaster structural evaluation using a terrestrial laser scanner. Integrating generations FIG working week 2008. Stockholm, Sweden, pp 1–15Google Scholar
  7. Chen J, Liu H, Zheng J, Lv M, Yan B, Hu X, Gao Y (2016) Damage degree evaluation of earthquake area using UAV aerial image. Int J Aerosp Eng 2016:1–10.  https://doi.org/10.1155/2016/2052603CrossRefGoogle Scholar
  8. Dominici D, Alicandro M, Massimi V (2017) UAV photogrammetry in the post-earthquake scenario: case studies in L’Aquila. Geomat Nat Hazards Risk 8:87–103.  https://doi.org/10.1080/19475705.2016.1176605CrossRefGoogle Scholar
  9. Dong L, Shan J (2013) A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J Photogramm Remote Sens 84:85–99.  https://doi.org/10.1016/j.isprsjprs.2013.06.011CrossRefGoogle Scholar
  10. Erkal BG (2017) The prototype of a software application for laser and image-based surface damage detection. In: Proceedings of the 2nd world congress on civil, structural, and environmental engineering (CSEE’17), pp 1–7Google Scholar
  11. Federal Emergency Management Agency (FEMA) (2016) Damage assessment operations manual-A guide to assessing damage and impactGoogle Scholar
  12. Fernandez Galarreta J, Kerle N, Gerke M (2015) UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning. Nat Hazards Earth Syst Sci 15:1087–1101.  https://doi.org/10.5194/nhess-15-1087-2015CrossRefGoogle Scholar
  13. Gomez C, Purdie H (2016) UAV-based photogrammetry and geocomputing for hazards and disaster risk monitoring–A review. Geoenvironmental Disasters 3:23.  https://doi.org/10.1186/s40677-016-0060-yCrossRefGoogle Scholar
  14. Grünthal G (1998) European Macroseismic Scale 1998. Chaiers du Centre Européen de Géodynamique et de Séismologie, LuxembourgGoogle Scholar
  15. Guldur B, Hajjar J (2016) Automated classification of detected surface damage from point clouds with supervised learning. In: Proceedings of the 33rd ISARC. Auburn, AL, USA, pp 307–313Google Scholar
  16. Guldur Erkal B, Hajjar JF (2017) Laser-based surface damage detection and quantification using predicted surface properties. Autom Constr 83:285–302.  https://doi.org/10.1016/j.autcon.2017.08.004CrossRefGoogle Scholar
  17. Jafari B, Khaloo A, Lattanzi D (2017) Deformation tracking in 3D point clouds via statistical sampling of direct cloud-to-cloud distances. J Nondestruct Eval 36:65.  https://doi.org/10.1007/s10921-017-0444-2CrossRefGoogle Scholar
  18. Kayen R, Collins BD, Bawden G, Pack RT (2006) Earthquake deformation analysis using terrestrial scanning Laser-LIDAR technology. In: 8th US national conference on earthquake engineering. San Francisco, California, USAGoogle Scholar
  19. Kiratzi A (2018) The 12 June 2017 Mw 6.3 Lesvos Island (Aegean Sea) earthquake: slip model and directivity estimated with finite-fault inversion. Tectonophysics 724–725:1–10.  https://doi.org/10.1016/j.tecto.2018.01.003CrossRefGoogle Scholar
  20. Mukupa W, Roberts GW, Hancock CM, Al-Manasir K (2016) A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures. Surv Rev 49:99–116.  https://doi.org/10.1080/00396265.2015.1133039CrossRefGoogle Scholar
  21. Nimodia C, Deshmukh HR (2012) Android operating system. Softw Eng 3:10–13Google Scholar
  22. Olsen MJ, Chen Z, Hutchinson T, Kuester F (2013) Optical techniques for multiscale damage assessment. Geomat Nat Hazards Risk 4:49–70.  https://doi.org/10.1080/19475705.2012.670668CrossRefGoogle Scholar
  23. Olsen MJ, Cheung KF, YamazakI Y, Butcher S, Garlock M, Yim S, McGarity S, Robertson I, Burgos L, Young YL (2012) Damage assessment of the 2010 Chile earthquake and tsunami using terrestrial laser scanning. Earthq Spectra 28:S179–S197.  https://doi.org/10.1193/1.4000021CrossRefGoogle Scholar
  24. Olsen MJ, Kayen R (2012) Post-earthquake and tsunami 3D laser scanning forensic investigations. Forensic engineering 2012. American Society of Civil Engineers, Reston, VA, pp 477–486CrossRefGoogle Scholar
  25. Papadimitriou P, Kassaras I, Kaviris G, Tselentis G-A, Voulgaris N, Lekkas E, Chouliaras G, Evangelidis C, Pavlou K, Kapetanidis V, Karakonstantis A, Kazantzidou-Firtinidou D, Fountoulakis I, Millas C, Spingos I, Aspiotis T, Moumoulidou A, Skourtsos E, Antoniou V, Andreadakis E, Mavroulis S, Kleanthi M (2018) The 12th June 2017 Mw = 6.3 Lesvos earthquake from detailed seismological observations. J Geodyn 115:23–42.  https://doi.org/10.1016/j.jog.2018.01.009CrossRefGoogle Scholar
  26. Papakonstantinou A, Doukari M, Moustakas A, Chrisovalantis D, Chaidas K, Roussou O, Athanasis N, Topouzelis K, Soulakellis N (2018) UAS multi-camera rig for post-earthquake damage 3D geovisualization of Vrisa village. In: Themistocleous K, Hadjimitsis DG, Michaelides S, Ambrosia V, Papadavid G (eds) Sixth international conference on remote sensing and geoinformation of the environment (RSCy2018). SPIE, p 52Google Scholar
  27. Pesci A, Teza G, Bonali E, Casula G, Boschi E (2013) A laser scanning-based method for fast estimation of seismic-induced building deformations. ISPRS J Photogramm Remote Sens 79:185–198.  https://doi.org/10.1016/j.isprsjprs.2013.02.021CrossRefGoogle Scholar
  28. Puente I, Lindenbergh R, Van Natijne A, Esposito R, Schipper R (2018) Monitoring of progressive damage in buildings using laser scan data. In: ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XLII-2:923–929.  https://doi.org/10.5194/isprs-archives-xlii-2-923-2018CrossRefGoogle Scholar
  29. Schütz M (2016) Potree: rendering large point clouds in web. Master Thesis, Vienna University of TechnologyGoogle Scholar
  30. Snavely N (2011) Scene reconstruction and visualization from internet photo collections: a survey. IPSJ Trans Comput Vis Appl 3:44–66.  https://doi.org/10.2197/ipsjtcva.3.44CrossRefGoogle Scholar
  31. Snavely N, Seitz SM, Szeliski R (2008) Modeling the world from internet photo collections. Int J Comput Vis 80:189–210.  https://doi.org/10.1007/s11263-007-0107-3CrossRefGoogle Scholar
  32. Song M, Yousefianmoghadam S, Mohammadi M-E, Moaveni B, Stavridis A, Wood RL (2018) An application of finite element model updating for damage assessment of a two-story reinforced concrete building and comparison with lidar. Struct Heal Monit 17:1129–1150.  https://doi.org/10.1177/1475921717737970CrossRefGoogle Scholar
  33. Triggs B, McLauchlan PF, Hartley RI, Fitzgibbon AW (2000) Bundle adjustment—a modern synthesis. In: Lecture Notes in Computer Science, pp 298–372Google Scholar
  34. Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012) ‘Structure-from-Motion’ photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300–314.  https://doi.org/10.1016/j.geomorph.2012.08.021CrossRefGoogle Scholar
  35. Xu Z, Yang J, Peng C, Wu Y, Jiang X, Li R, Zheng Y, Gao Y, Liu S, Tian B (2014) Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China. Comput Geosci 68:22–30.  https://doi.org/10.1016/j.cageo.2014.04.001CrossRefGoogle Scholar
  36. Yamazaki F, Matsuda T, Denda S, Liu W (2015) Construction of 3D models of buildings damaged by earthquakes using UAV aerial images. In: Proceedings of the tenth pacific conference earthquake engineering building an earthquake-resilient pacificGoogle Scholar
  37. Zhao X, Kargoll B, Omidalizarandi M, Xu X, Alkhatib H (2018) Model selection for parametric surfaces approximating 3D point clouds for deformation analysis. Remote Sens 10:634.  https://doi.org/10.3390/rs10040634CrossRefGoogle Scholar
  38. Zhihua X, Lixin W, Yonglin S, Qiuling W, Ran W, Fashuai L (2014) Extraction of damaged building’s geometric features from multi-source point clouds. In: 2014 IEEE geoscience and remote sensing symposium, IEEE, pp 4764–4767Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nikolaos Soulakellis
    • 1
    Email author
  • Georgios Tataris
    • 1
  • Ermioni-Eirini Papadopoulou
    • 1
  • Stamatis Chatzistamatis
    • 2
  • Christos Vasilakos
    • 1
  • Dimitris Kavroudakis
    • 1
  • Olga Roussou
    • 1
  • Apostolos Papakonstantinou
    • 1
  1. 1.Department of GeographyUniversity of the AegeanMytileneGreece
  2. 2.Department of Cultural Technology and CommunicationUniversity of the AegeanMytileneGreece

Personalised recommendations