1,520
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Using remote sensing for exposure and seismic vulnerability evaluation: is it reliable?

ORCID Icon, , , &
Article: 2196162 | Received 28 Sep 2022, Accepted 24 Mar 2023, Published online: 12 Apr 2023

References

  • SDG-United Nations: https://sdgs.un.org/goals/goal11. Accessed 24/12/2023
  • Arredondo, A. 2019. GitHub repositoy. Haiti OBIA. https://github.com/arredond/haiti-obia.
  • Bhattacharjee, A., and R. Lossio. 2011. “Evaluation of OCHA Response to theHaiti Earthquake.” Final Report January.
  • Bilham, R. 2010. “Lessons from the Haiti Earthquake.” Nature 463 (7283): 878–33. doi:10.1038/463878a.
  • Bittner, K., F. Adam, S. Cui, M. Koerner, and P. Reinartz. 2018. “Building Footprint Extraction from VHR Remote Sensing Images CombinedWith Normalized DSMs Using Fused Fully Convolutional Networks.” Ieee Journal ofSelected Topics in Applied Earth Observations and Remote Sensing 11 (8): 2615–2629. doi:10.1109/jstars.2018.2849363.
  • Boore, D. M., J. P. Stewart, E. Seyhan, and G. M. Atkinson. 2014. “NGA-West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for ShallowCrustal Earthquakes.” Earthquake Spectra 30 (3): 1057–1085. doi:10.1193/070113eqs184m.
  • Borfecchia, F., M. Pollino, L. De Cecco, A. Lugari, S. Martini, L. La Porta, E. Ristoratore, and C. Pascale. 2010. “Active and Passive Remote Sensing for Supporting The evaluation of the Urban Seismic Vulnerability.” Rivista Italiana Di Telerilevamento 42 (3): 129–141. doi:10.5721/ItJRS201042310.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/a:1010933404324.
  • Cerri, M. 2018. “Multivariable Flood Damage Modelling for Residential Buildings using Open Data with Random Forests.“ Technische Universität München. 82. https://elib.dlr.de/125790/.
  • Chiou, B. S. J., and R. R. Youngs. 2014. “Update of the Chiou and Youngs NGAModel for the Average Horizontal Component of Peak Ground Motion andResponse Spectra.” Earthquake Spectra 30 (3): 1117–1153. doi:10.1193/072813eqs219m.
  • Corbane, C., K. Saito, L. Dell’oro, E. Bjorgo, S. P. D. Gill, B. Piard, C. K. Huyck, et al. 2011. “A Comprehensive Analysis of Building Damage in the 12 January 2010 M(W)7 Haiti Earthquake Using High-Resolution Satellite- and Aerial Imagery.” Photogrammetric Engineering & Remote Sensing 77 (10): 997–1009. doi:10.14358/pers.77.10.0997.
  • Costanzo, A., A. Montuori, J. Pablo Silva, M. Silvestri, F. D. MassimoMusacchio, S. Stramondo, M. Fabrizia Buongiorno, and M. Buongiorno. 2016. “The Combined Use of Airborne Remote Sensing Techniques Within a GISEnvironment for the Seismic Vulnerability Assessment of Urban Areas: AnOperational Application.” Remote Sensing 8 (2): 146. doi:10.3390/rs8020146.
  • Cox, B. R., J. Bachhuber, E. Rathje, C. M. Wood, R. Dulberg, R. A. G. AlbertKottke, S. M. Olson, and S. M. Olson. 2011. “Shear Wave Velocity- andGeology-Based Seismic Microzonation of Port-Au-Prince, Haiti.” EarthquakeSpectra 27 (1_suppl1): S67–92. doi:10.1193/1.3630226.
  • CRED, UNISDR. 2016. “Poverty & Death: Disaster Mortality, 1996–2015.” In Centre for Research on the Epidemiology of Disasters, Brussels, Belgium.
  • Dell’acqua, F., P. Gamba, and K. Jaiswal. 2013. “Spatial Aspects of Building And population Exposure Data and Their Implications for Global Earthquake Exposure Modeling.” Natural Hazards 68 (3): 1291–1309. doi:10.1007/s11069-012-0241-2.
  • De Los Santos, M. J. D., and J. A. Principe. 2021. “Gis-Based Rapid Earthquake Exposure and Vulnerability Mapping Using Lidar Dem and Machine Learning Algorithms: Case of Porac, Pampanga.” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 46, 17–19 November 2021.
  • Dustin, M., T. Kijewski-Correa, and A. A. Taflanidis. 2011. “Assessment of Residential Housing in Léogâne, Haiti, and Identification of Needs for Rebuilding After the January 2010 Earthquake.” Earthquake Spectra 27 (1_suppl1): S299–322. doi:10.1193/1.3637942.
  • Eberhard, M. O., S. Baldridge, J. Marshall, W. Mooney, and G. J. Rix. 2010. “The Mw 7.0 Haiti Earthquake of January 12, 2010: USGS/EERI Advance reconnaissance Team Report.” US Geological Survey Open-File Report 1048 (2013). doi: https://pubs.usgs.gov/of/2010/1048/.
  • Ehrlich, D., T. Kemper, X. Blaes, and P. Soille. 2013. “Extracting Building Stock information from Optical Satellite Imagery for Mapping Earthquake Exposure And its Vulnerability.” Natural Hazards 68 (1): 79–95. doi:10.1007/s11069-012-0482-0.
  • Fan, X., G. Nie, C. Xia, and J. Zhou. 2021. “Estimation of Pixel-Level Seismic Vulnerability of the Building Environment Based on Mid-Resolution Optical Remote Sensing Images.” International Journal of Applied Earth Observation and Geoinformation 101: 102339. doi:10.1016/j.jag.2021.102339.
  • Fawcett, T. 2006. “An Introduction to ROC Analysis.” Pattern Recognition Letters 27 (8): 861–874. doi:10.1016/j.patrec.2005.10.010.
  • Felzenszwalb, P. F., and D. P. Huttenlocher. 2004. “Efficient Graph-Based Imagesegmentation.” International Journal of Computer Vision 59 (2): 167–181. doi:10.1023/b:visi.0000022288.19776.77.
  • FEMA-178. 1992. “NEHRP Handbook for the Seismic Evaluation of Existing Buildings”. FEMA 178, Federal Emergency Management Agency, Washington, D.C., USA.
  • FEMA-440. 2005. “Improvement of Nonlinear Static Seismic Analysis Procedures”. FEMA 440, Federal Emergency Management Agency, Washington D.C.
  • Fierro, E., and C. Perry. 2010. “Preliminary Reconnaissance Report.” BFP Engineers 12. January 12, 2010. https://apps.peer.berkeley.edu/publications/haiti_2010/documents/Haiti_Reconnaissance.pdf.
  • GAR. 2009. “Global Assessment Report on Disaster Risk Reduction”. United Nations International Strategy for Disaster Reduction Secretariat (UNISDR). ISBN/ISSN: 9789211320282. 207pp
  • Geiss, C., P. Aravena Pelizari, M. Marconcini, W. Sengara, T. L. MarkEdwards, H. Taubenboeck, and H. Taubenböck. 2015. “Estimation of Seismic building Structural Types Using Multi-Sensor Remote Sensing and Machine Learningtechniques.” ISPRS Journal of Photogrammetry and Remote Sensing 104: 175–188. doi:10.1016/j.isprsjprs.2014.07.016.
  • Geiss, C., M. Jilge, T. Lakes, and H. Taubenboeck. 2016. “Estimation of Seismic Vulnerability Levels of Urban Structures with MultisensorRemote Sensing.” Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (5): 1913–1936. doi:10.1109/jstars.2015.2442584.
  • Geiss, C., A. Schauss, T. Riedlinger, S. Dech, C. Zelaya, M. A. H. NicolasGuzman, J. Jokar Arsanjani, H. Taubenboeck, and H. Taubenböck. 2017. “Joint Use of Remote Sensing Data and Volunteered Geographic Information for Exposure Estimation: Evidence from Valparaíso, Chile.” Natural Hazards 86 (86): 81–105. doi:10.1007/s11069-016-2663-8.
  • Grünthal, G. 1998. European Macroseismic Scale 1998. In: European Seismological Commission (ESC).
  • Guiwu, S., Q. Wenhua, S. Zhang, T. Sim, X. Liu, R. Sun, L. Sun, and Y. Jin. 2015. “An Integrated Method Combining Remote Sensing Data andLocal Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan,china.” Remote Sensing 7 (3): 2543–2601. doi:10.3390/rs70302543.
  • Hancilar, U., F. Taucer, and C. Corbane. 2013. “Empirical Fragility Functions based on Remote Sensing and Field Data After the 12 January 2010 HaitiEarthquake.” Earthquake Spectra 29 (4): 1275–1310. doi:10.1193/121711eqs308m.
  • Hao, W., Z. Cheng, W. Shi, Z. Miao, and X. Chenchen. 2014. “An object-Based Image Analysis for Building Seismic Vulnerability Assessment Using high-Resolution Remote Sensing Imagery.” Natural Hazards 71 (1): 151–174. doi:10.1007/s11069-013-0905-6.
  • Hayes, G. P., E. K. Meyers, J. W. Dewey, R. W. Briggs, P. S. Earle, H. M. Benz, G. M. Smoczyk, H. E. Flamme, W. D. Barnhart, and R. D. Gold. 2017. Tectonic Summaries of Magnitude 7 and Greater Earthquakes from 2000 to 2015. In: US Geological Survey.
  • Kononenko, I. 1994. Estimating Attributes: Analysis and Extensions of RELIEF. Paperpresented at the European conference on machine learning, Catania, Italy, April 6–8, 1994.
  • Lagomarsino, S., and S. Giovinazzi. 2006. “Macroseismic and Mechanical Models for the Vulnerability and Damage Assessment of Current Buildings.” Bulletin of Earthquake Engineering 4 (4): 415–443. doi:10.1007/s10518-006-9024-z.
  • Lallemant, D. 2014. “Waiting for the Big One: The Continued Earthquake Risk of Port-Au-Prince, Haiti”. In 3rd International Conference on Urban Disaster Recovery at Boulder, Colorado. DOI: 10.13140/2.1.2057.8087
  • Landis, J. R., and G. G. Koch. 1977. “Measurement of Observer Agreement For categorical Data.” Biometrics 33 (1): 159–174. doi:10.2307/2529310.
  • Liuzzi, M., P. Aravena Pelizari, C. Geiss, A. Masi, V. Tramutoli, and H. Taubenboeck. 2019. “A Transferable Remote Sensing approach to Classify Building Structural Types for Seismic Risk Analyses: The Case ofVal d’Agri Area (Italy).” Bulletin of Earthquake Engineering 17 (9): 4825–4853. doi:10.1007/s10518-019-00648-7.
  • Milutinovic, Z. V., and G. S. Trendafiloski. 2003. “Risk-UE An advanced approach to earthquake risk scenarios with applications to different european towns.” In Contract: EVK4-CT-2000-00014, 1–111. WP4: Vulnerability of Current Buildings. http://www.civil.ist.utl.pt/~mlopes/conteudos/DamageStates/Risk%20UE%20WP04_Vbility.pdf.
  • Molina, S., D. H. Lang, and C. D. Lindholm. 2010. “SELENA - An Open-Source Tool For seismic Risk and Loss Assessment Using a Logic Tree Computation procedure.”Computers &.” Geosciences 36 (3): 257–269. doi:10.1016/j.cageo.2009.07.006.
  • Molina, S., Y. Torres, B. Benito, M. Navarro, and D. Belizaire. 2014. “Using the Damage from 2010 Haiti Earthquake for Calibrating Vulnerability Models of Typical structures in Port-Au-Prince (Haiti).” Bulletin of Earthquake Engineering 12 (4): 1459–1478. doi:10.1007/s10518-013-9563-z.
  • Moya, L., C. Geis, M. Hashimoto, E. Mas, S. Koshimura, and G. Strunz. 2021. “Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (10): 8288–8304. doi:10.1109/tgrs.2020.3046004.
  • MTPTC 2010. “Evaluation des Batiments”. In: Ministere des Travaux Publics, Transports et Communications. http://www.mtptc.gouv.ht/accueil/actualites/article_7.html. Accessed 17/03/2022.
  • Mueck, M., H. Taubenboeck, J. Post, S. Wegscheider, G. Strunz, S. Sumaryono, and F. A. Ismail. 2013. “Assessing Building Vulnerability to Earthquake and Tsunami Hazard using Remotely Sensed Data.” Natural Hazards 68 (1): 97–114. doi:10.1007/s11069-012-0481-1.
  • Panagiota, M., C. Jocelyn, P. Erwan, G. Philippe, and Ieee.2012. “A Support Vector Regression Approach for Building Seismic vulnerability Assessment and Evaluation from Remote Sensing Andin-Situ Data.” 2012 Ieee International Geoscience and Remote SensingSymposium (Igarss):7533–7536. doi: 10.1109/igarss.2012.6351888.
  • Pittore, M., and M. Wieland. 2013. “Toward a Rapid Probabilistic Seismic vulnerability Assessment Using Satellite and Ground-Based Remote sensing.”Natural Hazards.” Natural Hazards 68 (1): 115–145. doi:10.1007/s11069-012-0475-z.
  • Polli, D., and F. Dell’acqua. 2011. “Fusion of Optical and SAR Data for SeismicVulnerability Mapping of Buildings.” Optical Remote Sensing: Advances in SignalProcessing and Exploitation Techniques 3: 329–341. doi:10.1007/978-3-642-14212-3_15.
  • Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers.
  • Riedel, I., P. Gueguen, M. Dalla Mura, E. Pathier, T. Leduc, and J. Chanussot. 2015. “Seismic Vulnerability Assessment of Urban environments in Moderate-To-Low Seismic Hazard Regions Using Association Rule learning and Support Vector Machine Methods.” Natural Hazards 76 (2): 1111–1141. doi:10.1007/s11069-014-1538-0.
  • Riedel, I., P. Gueguen, F. Dunand, and S. Cottaz. 2014. “Macroscale VulnerabilityAssessment of Cities Using Association Rule Learning.” Seismological ResearchLetters 85 (2): 295–305. doi:10.1785/0220130148.
  • Ruggieri, S., A. Cardellicchio, V. Leggieri, and G. Uva. 2021. “Machine-Learning Based Vulnerability Analysis of Existing Buildings.” Automation in Construction 132: 103936. doi:10.1016/j.autcon.2021.103936.
  • Sarabandi, P., A. Kiremidjian, R. T. Eguchi, and B. J. Adams. 2008. “Building Inventory Compilation for Disaster Management: Application of Remote sensing and Statistical Modeling.“ Technical Report Series MCEER-08-0025. 132. Buffalo: MCEER. https://www.eng.buffalo.edu/mceer-reports/08/08-0025.pdf .
  • Shunping, J., S. Wei, and L. Meng. 2019. “Fully Convolutional Networks forMultisource Building Extraction from an Open Aerial and Satellite Imagery DataSet.” IEEE Transactions on Geoscience and Remote Sensing 57 (1): 574–586. doi:10.1109/tgrs.2018.2858817.
  • Silva, V., P. Henshaw, C. Huyck, and M. O’hara. 2018. “GED4ALL Global Exposure Database for Multi-Hazard Risk Analysis D5-FINAL REPORT.” In.: GEM Technical Report 2018-05. Pavia: GEM Foundation.
  • Sohn, G., and I. Dowman. 2007. “Data Fusion of High-Resolution Satellite Imagery and LiDar Data for Automatic Building Extraction.” Isprs Journal ofPhotogrammetry and Remote Sensing 62 (1): 43–63. doi:10.1016/j.isprsjprs.2007.01.001.
  • Stepinac, M., and M. Gasparovic. 2020. “A Review of Emerging Technologies for an Assessment of Safety and Seismic Vulnerability and Damage Detection of Existing Masonry Structures.” Applied Sciences-Basel 10 (15): 5060. doi:10.3390/app10155060.
  • Symithe, S., and E. Calais. 2016. “Present-Day Shortening in Southern Haiti fromGps Measurements and Implications for Seismic Hazard.” Tectonophysics 679 (679): 117–124. doi:10.1016/j.tecto.2016.04.034.
  • Taubenböck, H., A. Roth, S. Dech, H. Mehl, J. C. Münich, L. Stempniewski, and J. Zschau. 2009. “Assessing Building Vulnerability Using Synergistically Remotesensing and Civil Engineering.” In Urban and Regional Data Management, edited by Alenka Krek, Massimo Rumor, Sisi Zlatanova, Elfriede M. Fendel, 299–312. London: CRC Press.
  • Template - Building In-Field Survey. Torres, Yolanda (2022). Mendeley Data, V2, doi: 10.17632/rgmtgdnkjb.2
  • Torres, Y., S. Molina, S. Martinez-Cuevas, M. Navarro, J. J. Martinez-Diaz, B. Benito, J. J. Galiana-Merino, and D. Belizaire. 2016. “A First Approach to Earthquake damage Estimation in Haiti: Advices to Minimize the Seismic Risk.” Bulletin ofEarthquake Engineering 14 (1): 39–58. doi:10.1007/s10518-015-9813-3.
  • Ustun, B., W. J. Melssen, and L. M. C. Buydens. 2006. “Facilitating the Application ofSupport Vector Regression by Using a Universal Pearson VII Function Basedkernel.” Chemometrics and Intelligent Laboratory Systems 81 (1): 29–40. doi:10.1016/j.chemolab.2005.09.003.
  • Vapnik, V. 2000. “The Nature of Statistical Learning Theory”. 2nd ed. New York, NY, USA: Springer. doi:10.1007/978-1-4757-3264-1.
  • Wenhua, Q., S. Guiwu, L. Sun, F. Yang, and W. Yang. 2017. ““Internet+” Approach to Mapping Exposure and Seismic Vulnerability of Buildings in a Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China.” Natural Hazards 86 (S1): 107–139. doi:10.1007/s11069-016-2581-9.
  • Wieland, M. 2013. “Exposure Estimation for Rapid Seismic Vulnerability Assessment: An Integrated Approach Based on Multi-Source Imaging.“ PhD. Thesis, EWS Centre for EarlyWarning, Geoengineering Centres, GFZ Publication. https://doi.org/10.14279/depositonce-3844.
  • Wieland, M., and M. Pittore. 2014. “Performance Evaluation of MachineLearning Algorithms for Urban Pattern Recognition from Multi-Spectral SatelliteImages.” Remote Sensing 6 (4): 2912–2939. doi:10.3390/rs6042912.
  • Wieland, M., M. Pittore, S. Parolai, and J. Zschau. 2012. “Exposure Estimation from Multi-Resolution Optical Satellite Imagery for SeismicRisk Assessment.” ISPRS International Journal of Geo-Information 1 (1): 69–88. doi:10.3390/ijgi1010069.
  • Wieland, M., M. Pittore, S. Parolai, J. Zschau, B. Moldobekov, and U. Begaliev. 2012. “Estimating Building Inventory for Rapid Seismic Vulnerability Assessment: Towards an Integrated Approach Based on Multi-Source Imaging.” Soil Dynamics andEarthquake Engineering 36: 70–83. doi:10.1016/j.soildyn.2012.01.003.
  • Wieland, M., Y. Torres, M. Pittore, and B. Benito. 2016. “Object-Based Urban Structure Type Pattern Recognition from Landsat TM with a SupportVector Machine.” International Journal of Remote Sensing 37 (17): 4059–4083. doi:10.1080/01431161.2016.1207261.
  • Yolanda, T., J. Juan Arranz, J. M. Gaspar-Escribano, A. Haghi, B. B. SandraMartinez-Cuevas, J. Carlos Ojeda, and J. C. Ojeda. 2019. “Integration ofLidar and Multispectral Images for Rapid Exposure and Earthquake Vulnerability estimation. Application in Lorca, Spain.” International Journal of Applied EarthObservation and Geoinformation 81: 161–175. doi:10.1016/j.jag.2019.05.015.
  • Yongyang, X., W. Liang, Z. Xie, and Z. Chen. 2018. “Building Extraction inVery High Resolution Remote Sensing Imagery Using Deep Learning and GuidedFilters.” Remote Sensing 10 (1): 144. doi:10.3390/rs10010144.
  • Zhou, J., Y. Liu, G. Nie, H. Cheng, X. Yang, X. Chen, and L. Gross. 2022. “Building Extraction and Floor Area Estimation at the Village Level in Rural China via a Comprehensive Method Integrating UAV Photogrammetry and the Novel EDSANet.” Remote Sensing 14 (20): 5175. doi:10.3390/rs14205175.