{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:23:22Z","timestamp":1742945002908,"version":"3.40.3"},"publisher-location":"Cham","reference-count":205,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030824297"},{"type":"electronic","value":"9783030824303"}],"license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-82430-3_14","type":"book-chapter","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T13:05:50Z","timestamp":1638450350000},"page":"315-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Smart Disaster Risk Reduction and Emergency Management in the Built Environment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2372-6440","authenticated-orcid":false,"given":"Xavier","family":"Rom\u00e3o","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9602-2452","authenticated-orcid":false,"given":"Fernando Lobo","family":"Pereira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,3]]},"reference":[{"key":"14_CR1","unstructured":"CRED: Natural disasters 2019: Now is the time to not give up. Universit\u00e9 Catholique de Louvain, Centre for Research on the Epidemiology of Disasters, Brussels, Belgium (2020)"},{"key":"14_CR2","unstructured":"United Nations General Assembly: Transforming our world\u2014the 2030 agenda for sustainable development, outcome document of the United Nations summit for the adoption of the post-2015 agenda, RES\/A\/70\/L.1. United Nations, New York (2015)"},{"key":"14_CR3","volume-title":"Disruptive Technologies and Their Use in Disaster Risk Reduction and MANAGEMENT","author":"M Minges","year":"2019","unstructured":"Minges, M.: Disruptive Technologies and Their Use in Disaster Risk Reduction and MANAGEMENT. International Telecommunication Union, Geneva (2019)"},{"key":"14_CR4","unstructured":"UNISDR: Progress and Challenges in Disaster Risk Reduction: A Contribution Towards the Development of Policy Indicators for the Post-2015 Framework for Disaster Risk Reduction. United Nations Office for Disaster Risk Reduction (2014)"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Enia, J.: Is there an international disaster risk reduction regime? Does it matter? Progr. Disaster Sci. 100098 (2020)","DOI":"10.1016\/j.pdisas.2020.100098"},{"key":"14_CR6","unstructured":"McEntire, D. A.: Disaster response and recovery: strategies and tactics for resilience. John Wiley & Sons (2015)"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Ranghieri, F., Ishiwatari, M. (Eds.): Learning from megadisasters: lessons from the Great East Japan Earthquake. The World Bank (2014)","DOI":"10.1596\/978-1-4648-0153-2"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Hochrainer-Stigler, S., Colon, C., Boza, G., Poledna, S., Rovenskaya, E., Dieckmann, U.: Enhancing resilience of systems to individual and systemic risk: steps toward an integrative framework. Int. J. Disaster Risk Reduct. 101868 (2020)","DOI":"10.1016\/j.ijdrr.2020.101868"},{"key":"14_CR9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1146\/annurev-soc-073014-112317","volume":"41","author":"MA Centeno","year":"2015","unstructured":"Centeno, M.A., Nag, M., Patterson, T.S., Shaver, A., Windawi, A.J.: The emergence of global systemic risk. Ann. Rev. Sociol. 41, 65\u201385 (2015)","journal-title":"Ann. Rev. Sociol."},{"issue":"2","key":"14_CR10","first-page":"92","volume":"11","author":"M Mazzocchi","year":"2010","unstructured":"Mazzocchi, M., Hansstein, F., Ragona, M.: The 2010 volcanic ash cloud and its financial impact on the European airline industry. CESifo Forum 11(2), 92\u2013100 (2010)","journal-title":"CESifo Forum"},{"key":"14_CR11","unstructured":"Chongvilaivan, A.: Thailand\u2019s 2011 flooding: its impact on direct exports, and disruption of global supply chains. ARTNeT Working Paper No. 113. Bangkok, Thailand: UNESCAP (2012)"},{"key":"14_CR12","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ijsu.2020.04.018","volume":"78","author":"M Nicola","year":"2020","unstructured":"Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., Agha, R.: The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int. J. Surg. 78, 185\u2013193 (2020)","journal-title":"Int. J. Surg."},{"issue":"2","key":"14_CR13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10584-017-2048-3","volume":"144","author":"ME Mann","year":"2017","unstructured":"Mann, M.E., Lloyd, E.A., Oreskes, N.: Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach. Clim. Change 144(2), 131\u2013142 (2017)","journal-title":"Clim. Change"},{"issue":"1","key":"14_CR14","first-page":"33","volume":"13","author":"D Hasse","year":"2018","unstructured":"Hasse, D., Gauthier, F.A., de Rolt, C.R., Klein, G.H.: Coordinating emergency response by competent teams. IADIS Int. J. Comput. Sci. Inf. Syst. 13(1), 33\u201351 (2018)","journal-title":"IADIS Int. J. Comput. Sci. Inf. Syst."},{"key":"14_CR15","doi-asserted-by":"crossref","DOI":"10.1201\/b11371","volume-title":"Designing for Situation Awareness: An Approach to User-Centered Design","author":"MR Endsley","year":"2016","unstructured":"Endsley, M.R., Jones, D.G.: Designing for Situation Awareness: An Approach to User-Centered Design, 2nd edn. CRC Press, Boca Raton (2016)","edition":"2"},{"issue":"2","key":"14_CR16","doi-asserted-by":"crossref","first-page":"629","DOI":"10.5194\/nhess-16-629-2016","volume":"16","author":"B Liu","year":"2016","unstructured":"Liu, B., Siu, Y.L., Mitchell, G.: Hazard interaction analysis for multi-hazard risk assessment: a systematic classification based on hazard-forming environment. Nat. Hazard. 16(2), 629\u2013642 (2016)","journal-title":"Nat. Hazard."},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Menoni, S., Boni, M. P.: A Systemic Approach for Dealing with Chained Damages Triggered by Natural Hazards in Complex Human Settlements (2020)","DOI":"10.1016\/j.ijdrr.2020.101751"},{"key":"14_CR18","unstructured":"De Grove, T., Poljansek, K., Ehrlich, D.: Recording Disaster Losses. Recommendations for a European Research. JRC Scientific and Policy reports. Joint Research Centre, European Commission (2013)"},{"issue":"2","key":"14_CR19","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s11069-016-2364-3","volume":"83","author":"X Rom\u00e3o","year":"2016","unstructured":"Rom\u00e3o, X., Paup\u00e9rio, E.: A framework to assess quality and uncertainty in disaster loss data. Nat. Hazards 83(2), 1077\u20131102 (2016)","journal-title":"Nat. Hazards"},{"issue":"1","key":"14_CR20","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1504\/IJEM.2014.061659","volume":"10","author":"E Danielsson","year":"2014","unstructured":"Danielsson, E., Alvinius, A., Larsson, G.: From common operating picture to situational awareness. Int. J. Emerg. Manage. 10(1), 28\u201347 (2014)","journal-title":"Int. J. Emerg. Manage."},{"key":"14_CR21","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1111\/risa.12156","volume":"34","author":"S Skakun","year":"2014","unstructured":"Skakun, S., Kussul, N., Shelestov, A., Kussul, O.: Flood hazard and flood risk assessment using a time series of satellite images: a case study in Namibia. Risk Anal. 34, 1521\u20131537 (2014)","journal-title":"Risk Anal."},{"issue":"4","key":"14_CR22","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.agrformet.2007.12.005","volume":"148","author":"M Yebra","year":"2008","unstructured":"Yebra, M., Chuvieco, E., Ria\u00f1o, D.: Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agric. For. Meteorol. 148(4), 523\u2013536 (2008)","journal-title":"Agric. For. Meteorol."},{"issue":"4","key":"14_CR23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3390\/geosciences6040045","volume":"6","author":"T Dahigamuwa","year":"2016","unstructured":"Dahigamuwa, T., Yu, Q., Gunaratne, M.: Feasibility study of land cover classification based on normalized difference vegetation index for landslide risk assessment. Geosciences 6(4), 45 (2016)","journal-title":"Geosciences"},{"key":"14_CR24","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11069-012-0482-0","volume":"68","author":"D Ehrlich","year":"2013","unstructured":"Ehrlich, D., Kemper, T., Blaes, X., Soille, P.: Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability. Nat. Hazards 68, 79\u201395 (2013)","journal-title":"Nat. Hazards"},{"issue":"2","key":"14_CR25","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/19479832.2014.1001879","volume":"6","author":"J Tian","year":"2015","unstructured":"Tian, J., Nielsen, A.A., Reinartz, P.: Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs. Int. J. Image Data Fusion 6(2), 155\u2013169 (2015)","journal-title":"Int. J. Image Data Fusion"},{"issue":"2","key":"14_CR26","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.clsr.2012.01.005","volume":"28","author":"RL Finn","year":"2012","unstructured":"Finn, R.L., Wright, D.: Unmanned aircraft systems: surveillance, ethics and privacy in civil applications. Comput. Law Secur. Rev. 28(2), 184\u2013194 (2012)","journal-title":"Comput. Law Secur. Rev."},{"key":"14_CR27","unstructured":"Matin, M. A., Islam, M. M.: Overview of wireless sensor network. Wireless Sensor Networks-Technology and Protocols, pp. 1\u20133 (2012). https:\/\/bit.ly\/34hC82G. Last accessed 2020\/11\/30"},{"issue":"6","key":"14_CR28","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.compenvurbsys.2012.03.002","volume":"36","author":"YE Aslan","year":"2012","unstructured":"Aslan, Y.E., Korpeoglu, I., Ulusoy, \u00d6.: A framework for use of wireless sensor networks in forest fire detection and monitoring. Comput. Environ. Urban Syst. 36(6), 614\u2013625 (2012)","journal-title":"Comput. Environ. Urban Syst."},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Nguyen, C. D., Tran, T. D., Tran, N. D., Huynh, T. H., Nguyen, D. T.: Flexible and efficient wireless sensor networks for detecting rainfall-induced landslides. Int. J. Distrib. Sens. Netw. 11(11), 235954 (2015)","DOI":"10.1155\/2015\/235954"},{"issue":"3","key":"14_CR30","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1111\/j.1467-8667.2012.00781.x","volume":"28","author":"X Hu","year":"2013","unstructured":"Hu, X., Wang, B., Ji, H.: A wireless sensor network-based structural health monitoring system for highway bridges. Comput. Aided Civil Infrastruct. Eng. 28(3), 193\u2013209 (2013)","journal-title":"Comput. Aided Civil Infrastruct. Eng."},{"issue":"3","key":"14_CR31","doi-asserted-by":"crossref","first-page":"183","DOI":"10.12989\/sss.2010.6.3.183","volume":"6","author":"RA Swartz","year":"2010","unstructured":"Swartz, R.A., Lynch, J.P., Zerbst, S., Sweetman, B., Rolfes, R.: Structural monitoring of wind turbines using wireless sensor networks. Smart Struct. Syst. 6(3), 183\u2013196 (2010)","journal-title":"Smart Struct. Syst."},{"issue":"1","key":"14_CR32","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MPRV.2017.11","volume":"16","author":"M Erdelj","year":"2017","unstructured":"Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F.: Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Comput. 16(1), 24\u201332 (2017)","journal-title":"IEEE Pervasive Comput."},{"key":"14_CR33","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.adhoc.2012.06.002","volume":"13","author":"IM Khalil","year":"2014","unstructured":"Khalil, I.M., Khreishah, A., Ahmed, F., Shuaib, K.: Dependable wireless sensor networks for reliable and secure humanitarian relief applications. Ad Hoc Netw. 13, 94\u2013106 (2014)","journal-title":"Ad Hoc Netw."},{"key":"14_CR34","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.adhoc.2012.06.006","volume":"13","author":"G Tuna","year":"2014","unstructured":"Tuna, G., Gungor, V.C., Gulez, K.: An autonomous wireless sensor network deployment system using mobile robots for human existence detection in case of disasters. Ad Hoc Netw. 13, 54\u201368 (2014)","journal-title":"Ad Hoc Netw."},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Patil, H. K., Chen, T. M.: Wireless sensor network security: The internet of things. In: Vacca, J.R. (Ed.) Computer and Information Security Handbook, 3rd Ed., 317\u2013337. Elsevier (2017)","DOI":"10.1016\/B978-0-12-803843-7.00018-1"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Aktas, M. S., Astekin, M.: Provenance aware run\u2010time verification of things for self\u2010healing Internet of Things applications. Concurr. Comput. Pract. Exp. 31(3), e4263 (2019)","DOI":"10.1002\/cpe.4263"},{"key":"14_CR37","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.adhoc.2015.05.014","volume":"36","author":"MS Farash","year":"2016","unstructured":"Farash, M.S., Turkanovi\u0107, M., Kumari, S., H\u00f6lbl, M.: An efficient user authentication and key agreement scheme for heterogeneous wireless sensor network tailored for the Internet of Things environment. Ad Hoc Netw. 36, 152\u2013176 (2016)","journal-title":"Ad Hoc Netw."},{"issue":"3","key":"14_CR38","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s10346-016-0744-0","volume":"14","author":"WM Abdulwahid","year":"2017","unstructured":"Abdulwahid, W.M., Pradhan, B.: Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides 14(3), 1057\u20131076 (2017)","journal-title":"Landslides"},{"key":"14_CR39","doi-asserted-by":"crossref","unstructured":"Gibson, L., Adeleke, A., Hadden, R., Rush, D.: Spatial metrics from LiDAR roof mapping for fire spread risk assessment of informal settlements in Cape Town, South Africa. Fire Safety J. 103053 (2020)","DOI":"10.1016\/j.firesaf.2020.103053"},{"key":"14_CR40","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.jhydrol.2017.08.009","volume":"553","author":"B Chen","year":"2017","unstructured":"Chen, B., Krajewski, W.F., Goska, R., Young, N.: Using LiDAR surveys to document floods: a case study of the 2008 Iowa flood. J. Hydrol. 553, 338\u2013349 (2017)","journal-title":"J. Hydrol."},{"key":"14_CR41","doi-asserted-by":"crossref","first-page":"143","DOI":"10.5194\/nhess-17-143-2017","volume":"17","author":"L Moya","year":"2017","unstructured":"Moya, L., Yamazaki, F., Liu, W., Yamada, M.: Detection of collapsed buildings due to the 2016 Kumamoto, Japan, earthquake from LiDAR data. Nat. Hazard. 17, 143\u2013156 (2017)","journal-title":"Nat. Hazard."},{"key":"14_CR42","doi-asserted-by":"crossref","unstructured":"Bisson, M., Spinetti, C., Neri, M., Bonforte, A.: Mt. Etna volcano high-resolution topography: airborne LiDAR modelling validated by GPS data. Int. J. Digit. Earth 9(7), 710\u2013732 (2016)","DOI":"10.1080\/17538947.2015.1119208"},{"issue":"3","key":"14_CR43","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1175\/WAF-D-14-00098.1","volume":"30","author":"S Goldenberg","year":"2015","unstructured":"Goldenberg, S., Gopalakrishnan, S., Tallapragada, V., Quirino, T., Marks, F., Jr., Trahan, S., Zhang, X., Atlas, R.: The 2012 triply nested, high-resolution operational version of the Hurricane Weather Research and Forecasting Model (HWRF): track and intensity forecast verifications. Weather Forecast. 30(3), 710\u2013729 (2015)","journal-title":"Weather Forecast."},{"issue":"23","key":"14_CR44","doi-asserted-by":"crossref","first-page":"9058","DOI":"10.1175\/JCLI-D-15-0216.1","volume":"28","author":"H Murakami","year":"2015","unstructured":"Murakami, H., Vecchi, G., Underwood, S., Delworth, T., Wittenberg, A., Anderson, W., Chen, J.-H., Gudgel, R., Harris, L., Lin, S.-J., Zeng, F.: Simulation and prediction of category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model. J. Clim. 28(23), 9058\u20139079 (2015)","journal-title":"J. Clim."},{"key":"14_CR45","doi-asserted-by":"crossref","unstructured":"Heitzler, M., Lam, J., Hackl, J., Adey, B., Hurni, L.: A simulation and visualization environment for spatiotemporal disaster risk assessments of network infrastructures. Cartographica Int. J. Geogr. Inf. Geovis. 52(4), 349\u2013363 (2017)","DOI":"10.3138\/cart.52.4.2017-0009"},{"key":"14_CR46","doi-asserted-by":"crossref","unstructured":"Lin, N., Shullman, E.: Dealing with hurricane surge flooding in a changing environment: part I. Risk assessment considering storm climatology change, sea level rise, and coastal development. Stochastic Environ. Res. Risk Assess. 31(9), 2379\u20132400 (2017)","DOI":"10.1007\/s00477-016-1377-5"},{"key":"14_CR47","unstructured":"Clare, R., Bradley, B., Sun, D., Bae, S., Mc Gann, C.: QuakeCoRE and NeSI\u2019s strategic partnership towards earthquake resilience via High Performance Computing. In: eResearch NZ Conference, New Zealand (2016)"},{"issue":"3","key":"14_CR48","doi-asserted-by":"crossref","first-page":"06017001","DOI":"10.1061\/(ASCE)NH.1527-6996.0000246","volume":"18","author":"E Rathje","year":"2017","unstructured":"Rathje, E., Dawson, C., Padgett, J., Pinelli, J., Stanzione, D., Adair, A., Arduino, P., Brandenberg, S., Cockerill, T., Dey, C., Esteva, M., Haan, F., Hanlon, M., Kareem, A., Lowes, L., Mock, S., Mosqueda, G.: DesignSafe: new cyberinfrastructure for natural hazards engineering. Nat. Hazard. Rev. 18(3), 06017001 (2017)","journal-title":"Nat. Hazard. Rev."},{"key":"14_CR49","doi-asserted-by":"crossref","unstructured":"Wang, F., Magoua, J., Li, N., Fang, D.: Assessing the impact of systemic heterogeneity on failure propagation across interdependent critical infrastructure systems. Int. J. Disaster Risk Reduct. 50, 101818 (2020)","DOI":"10.1016\/j.ijdrr.2020.101818"},{"key":"14_CR50","doi-asserted-by":"crossref","unstructured":"Dong, S., Yu, T., Farahmand, H., Mostafavi, A.: Probabilistic modeling of cascading failure risk in interdependent channel and road networks in urban flooding. Sustain. Cities Soc. 62, 102398 (2020)","DOI":"10.1016\/j.scs.2020.102398"},{"key":"14_CR51","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ecolmodel.2011.07.010","volume":"229","author":"L An","year":"2012","unstructured":"An, L.: Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol. Model. 229, 25\u201336 (2012)","journal-title":"Ecol. Model."},{"key":"14_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-020-04389-8","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Jia, G.: A novel agent-based model for tsunami evacuation simulation and risk assessment. Nat. Hazards (2020). https:\/\/doi.org\/10.1007\/s11069-020-04389-8","journal-title":"Nat. Hazards"},{"issue":"2","key":"14_CR53","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.ejor.2018.02.013","volume":"269","author":"S Aros","year":"2018","unstructured":"Aros, S., Gibbons, D.: Exploring communication media options in an inter-organizational disaster response coordination network using agent-based simulation. Eur. J. Oper. Res. 269(2), 451\u2013465 (2018)","journal-title":"Eur. J. Oper. Res."},{"key":"14_CR54","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.trd.2019.08.011","volume":"77","author":"E Hajhashemi","year":"2019","unstructured":"Hajhashemi, E., Murray-Tuite, P., Hotle, S., Wernstedt, K.: Using agent-based modeling to evaluate the effects of Hurricane Sandy\u2019s recovery timeline on the ability to work. Transp. Res. Part D Transp. Environ. 77, 506\u2013524 (2019)","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"14_CR55","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.envsoft.2016.09.006","volume":"86","author":"Z Sun","year":"2016","unstructured":"Sun, Z., Lorscheid, I., Millington, J., Lauf, S., Magliocca, N., Groeneveld, J., Balbi, S., Nolzen, H., M\u00fcller, B., Schulze, J., Buchmann, C.: Simple or complicated agent-based models? A complicated issue. Environ. Model. Softw. 86, 56\u201367 (2016)","journal-title":"Environ. Model. Softw."},{"key":"14_CR56","doi-asserted-by":"crossref","unstructured":"Batista e Silva, F., Lavalle, C., Koomen, E.: A procedure to obtain a refined European land use\/cover map. J. Land Use Sci. 8(3), 255\u2013283 (2013)","DOI":"10.1080\/1747423X.2012.667450"},{"key":"14_CR57","doi-asserted-by":"crossref","unstructured":"Freire, S., Aubrecht, C.: Integrating population dynamics into mapping human exposure to seismic hazard. Nat. Hazards Earth Syst. Sci. 12(11) (2012)","DOI":"10.5194\/nhess-12-3533-2012"},{"key":"14_CR58","doi-asserted-by":"crossref","unstructured":"Mohanty, M., Simonovic, S.: Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets. Sci. Total Environ. 143559 (2020)","DOI":"10.1016\/j.scitotenv.2020.143559"},{"key":"14_CR59","doi-asserted-by":"crossref","unstructured":"e Silva, F., Forzieri, G., Herrera, M., Bianchi, A., Lavalle, C., Feyen, L.: HARCI-EU, a harmonized gridded dataset of critical infra-structures in Europe for large-scale risk assessments. Sci. Data 6(1), 1\u201311 (2019)","DOI":"10.1038\/s41597-019-0135-1"},{"issue":"4","key":"14_CR60","doi-asserted-by":"crossref","first-page":"114","DOI":"10.3390\/ijgi6040114","volume":"6","author":"M Wieland","year":"2017","unstructured":"Wieland, M., Pittore, M.: A spatio-temporal building exposure database and information life-cycle management solution. ISPRS Int. J. Geo Inf. 6(4), 114 (2017)","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"14_CR61","doi-asserted-by":"crossref","unstructured":"Crowley, H., Despotaki, V., Rodrigues, D., Silva, V., Toma-Danila, D., Riga, E., Karatzetzou, A., Fotopoulou, S., Zugic, Z., Sousa, L., Ozcebe, S., Gamba, P.: Exposure model for European seismic risk assessment. Earthq. Spectra 36(1_suppl), 252\u2013273 (2020)","DOI":"10.1177\/8755293020919429"},{"issue":"4","key":"14_CR62","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1111\/risa.13212","volume":"39","author":"M Amadio","year":"2019","unstructured":"Amadio, M., Mysiak, J., Marzi, S.: Mapping socioeconomic exposure for flood risk assessment in Italy. Risk Anal. 39(4), 829\u2013845 (2019)","journal-title":"Risk Anal."},{"issue":"13","key":"14_CR63","doi-asserted-by":"crossref","first-page":"4067","DOI":"10.1002\/hyp.9947","volume":"28","author":"L Alfieri","year":"2014","unstructured":"Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., Feyen, L.: Advances in pan-European flood hazard mapping. Hydrol. Process. 28(13), 4067\u20134077 (2014)","journal-title":"Hydrol. Process."},{"key":"14_CR64","doi-asserted-by":"crossref","unstructured":"Pagani, M., Garcia-Pelaez, J., Gee, R., Johnson, K., Poggi, V., Silva, V., Simionato, M., Styron, R., Vigan\u00f2, D., Danciu, L., Monelli, D., Weatherill, G.: The 2018 version of the global earthquake model: hazard component. Earthq. Spectra 8755293020931866 (2020)","DOI":"10.1177\/8755293020931866"},{"key":"14_CR65","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2015.10.012","volume":"115","author":"S Li","year":"2016","unstructured":"Li, S., Dragicevic, S., Castro, F., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., Cheng, T.: Geospatial big data handling theory and methods: A review and research challenges. ISPRS J. Photogramm. Remote. Sens. 115, 119\u2013133 (2016)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"14_CR66","doi-asserted-by":"crossref","unstructured":"Lwin, K., Sekimoto, Y., Takeuchi, W., Zettsu, K.: City geospatial dashboard: IoT and big data analytics for geospatial solutions provider in disaster management. In: 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) (2019)","DOI":"10.1109\/ICT-DM47966.2019.9032921"},{"issue":"1\/2","key":"14_CR67","doi-asserted-by":"crossref","first-page":"5:1","DOI":"10.1147\/JRD.2020.2970903","volume":"64","author":"C Albrecht","year":"2020","unstructured":"Albrecht, C., Elmegreen, B., Gunawan, O., Hamann, H., Klein, L., Lu, S., Mariano, F., Siebenschuh, C., Schmude, J.: Next-generation geospatial-temporal information technologies for disaster management. IBM J. Res. Dev. 64(1\/2), 5:1 (2020)","journal-title":"IBM J. Res. Dev."},{"key":"14_CR68","doi-asserted-by":"crossref","unstructured":"Sastry N.: Crowdsourcing and social networks. In: Alhajj R., Rokne J. (Eds.) Encyclopedia of Social Network Analysis and Mining. Springer, New York (2018)","DOI":"10.1007\/978-1-4939-7131-2_259"},{"key":"14_CR69","doi-asserted-by":"crossref","unstructured":"Goodchild, M.: Citizens as sensors: the world of volunteered geography. Geo J. 69(4), 211\u2013221 (2007)","DOI":"10.1007\/s10708-007-9111-y"},{"volume-title":"OpenStreetMap in GIScience\u2014Experiences, Research and Applications","year":"2015","key":"14_CR70","unstructured":"Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (eds.): OpenStreetMap in GIScience\u2014Experiences, Research and Applications. Springer, Berlin (2015)"},{"issue":"23","key":"14_CR71","doi-asserted-by":"crossref","first-page":"L23404","DOI":"10.1029\/2006GL027768","volume":"33","author":"K Iwao","year":"2006","unstructured":"Iwao, K., Nishida, K., Kinoshita, T., Yamagata, Y.: Validating land cover maps with Degree Confluence Project information. Geophys. Res. Lett. 33(23), L23404 (2006)","journal-title":"Geophys. Res. Lett."},{"key":"14_CR72","doi-asserted-by":"crossref","unstructured":"Fritz, S., McCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., Kraxner, F, Obersteiner, M.: Geo-Wiki. Org: The use of crowdsourcing to improve global land cover. Remote Sens. 1(3), 345\u2013354 (2009)","DOI":"10.3390\/rs1030345"},{"key":"14_CR73","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.landurbplan.2019.01.001","volume":"184","author":"M Bubalo","year":"2019","unstructured":"Bubalo, M., van Zanten, B., Verburg, P.: Crowdsourcing geo-information on landscape perceptions and preferences: a review. Landsc. Urban Plan. 184, 101\u2013111 (2019)","journal-title":"Landsc. Urban Plan."},{"issue":"1","key":"14_CR74","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3390\/ijgi9010010","volume":"9","author":"D Ma","year":"2020","unstructured":"Ma, D., Fan, H., Li, W., Ding, X.: The state of mapillary: an exploratory analysis. ISPRS Int. J. Geo Inf. 9(1), 10 (2020)","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"14_CR75","doi-asserted-by":"crossref","first-page":"S98","DOI":"10.1111\/jfr3.12181","volume":"11","author":"E Hirata","year":"2018","unstructured":"Hirata, E., Giannotti, M., Larocca, A., Quintanilha, J.: Flooding and inundation collaborative mapping\u2013use of the Crowdmap\/Ushahidi platform in the city of Sao Paulo, Brazil. J. Flood Risk Manag. 11, S98\u2013S109 (2018)","journal-title":"J. Flood Risk Manag."},{"issue":"2","key":"14_CR76","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/15420353.2012.663739","volume":"8","author":"P Meier","year":"2012","unstructured":"Meier, P.: Crisis mapping in action: how open source software and global volunteer networks are changing the world, one map at a time. J. Map Geogr. Libr. 8(2), 89\u2013100 (2012)","journal-title":"J. Map Geogr. Libr."},{"issue":"2","key":"14_CR77","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1080\/15420353.2012.662471","volume":"8","author":"J Ziemke","year":"2012","unstructured":"Ziemke, J.: Crisis mapping: the construction of a new interdisciplinary field? J. Map Geogr. Libr. 8(2), 101\u2013117 (2012)","journal-title":"J. Map Geogr. Libr."},{"key":"14_CR78","doi-asserted-by":"crossref","unstructured":"B\u00fcscher, M., Liegl, M., Thomas, V.: Collective intelligence in crises. In: Social Collective Intelligence, pp. 243\u2013265. Springer, Cham (2014)","DOI":"10.1007\/978-3-319-08681-1_12"},{"issue":"6","key":"14_CR79","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.isprsjprs.2010.06.005","volume":"65","author":"C Heipke","year":"2010","unstructured":"Heipke, C.: Crowdsourcing geospatial data. ISPRS J. Photogramm. Remote. Sens. 65(6), 550\u2013557 (2010)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"14_CR80","doi-asserted-by":"crossref","unstructured":"Dos Santos Rocha, R., Widera, A., van den Berg, R., de Albuquerque, J., Helingrath, B.: Improving the involvement of digital volunteers in disaster management. In: Murayama, Y., Velev, D., Zlateva, P., Gonzalez, J. (eds.), Proceedings of the International Conference on Information Technology in Disaster Risk Reduction, 214\u2013224. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-68486-4_17"},{"issue":"1","key":"14_CR81","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1177\/0160323X15575184","volume":"47","author":"J Sievers","year":"2015","unstructured":"Sievers, J.: Embracing crowdsourcing: a strategy for state and local governments approaching \u201cWhole Community\u201d emergency planning. State and Local Government Review 47(1), 57\u201367 (2015)","journal-title":"State and Local Government Review"},{"key":"14_CR82","doi-asserted-by":"crossref","unstructured":"Nonnecke, B., Mohanty, S., Lee, A., Lee, J., Beckman, S., Mi, J., Krishnan, S., Roxas, R., Oco, N., Crittenden, C., Goldberg, K.: Malasakit 1.0: A participatory online platform for crowdsourcing disaster risk reduction strategies in the Philippines. In: 2017 IEEE Global Humanitarian Technology Conference (GHTC). IEEE (2017)","DOI":"10.1109\/GHTC.2017.8239265"},{"issue":"3","key":"14_CR83","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1504\/IJWBC.2011.041206","volume":"7","author":"A Acar","year":"2011","unstructured":"Acar, A., Muraki, Y.: Twitter for crisis communication: lessons learned from Japan\u2019s tsunami disaster. Int. J. Web Based Commun. 7(3), 392\u2013402 (2011)","journal-title":"Int. J. Web Based Commun."},{"key":"14_CR84","doi-asserted-by":"crossref","unstructured":"Sarma, D., Das, A., Bera, U.: Uncertain demand estimation with optimization of time and cost using Facebook disaster map in emergency relief operation. Appl. Soft Comput. 87, 105992 (2020)","DOI":"10.1016\/j.asoc.2019.105992"},{"key":"14_CR85","doi-asserted-by":"crossref","unstructured":"Bhuvana, N., Aram, I.: Facebook and WhatsApp as disaster management tools during the Chennai (India) floods of 2015. Int. J. Disaster Risk Reduct. 39, 101135 (2019)","DOI":"10.1016\/j.ijdrr.2019.101135"},{"key":"14_CR86","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.compenvurbsys.2016.01.006","volume":"59","author":"C Granell","year":"2016","unstructured":"Granell, C., Ostermann, F.: Beyond data collection: objectives and methods of research using VGI and geo-social media for disaster management. Comput. Environ. Urban Syst. 59, 231\u2013243 (2016)","journal-title":"Comput. Environ. Urban Syst."},{"issue":"1","key":"14_CR87","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/10095020.2018.1560044","volume":"22","author":"Y Yan","year":"2019","unstructured":"Yan, Y., Schultz, M., Zipf, A.: An exploratory analysis of usability of Flickr tags for land use\/land cover attribution. Geospat. Inform. Sci. 22(1), 12\u201322 (2019)","journal-title":"Geospat. Inform. Sci."},{"issue":"1","key":"14_CR88","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11069-016-2329-6","volume":"83","author":"Z Wang","year":"2016","unstructured":"Wang, Z., Ye, X., Tsou, M.: Spatial, temporal, and content analysis of Twitter for wildfire hazards. Nat. Hazards 83(1), 523\u2013540 (2016)","journal-title":"Nat. Hazards"},{"key":"14_CR89","doi-asserted-by":"publisher","DOI":"10.1080\/13669877.2019.1569098","author":"Y Yue","year":"2019","unstructured":"Yue, Y., Dong, K., Zhao, X., Ye, X.: Assessing wild fire risk in the United States using social media data. J. Risk Res. (2019). https:\/\/doi.org\/10.1080\/13669877.2019.1569098","journal-title":"J. Risk Res."},{"key":"14_CR90","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.techfore.2016.06.010","volume":"111","author":"P Panagiotopoulos","year":"2016","unstructured":"Panagiotopoulos, P., Barnett, J., Bigdeli, A., Sams, S.: Social media in emergency management: twitter as a tool for communicating risks to the public. Technol. Forecast. Soc. Chang. 111, 86\u201396 (2016)","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"14_CR91","doi-asserted-by":"crossref","unstructured":"Jamali, M., Nejat, A., Moradi, S., Ghosh, S., Cao, G., Jin, F.: Social media data and housing recovery following extreme natural hazards. Int. J. Disaster Risk Reduct. 51, 101788 (2020)","DOI":"10.1016\/j.ijdrr.2020.101788"},{"issue":"2","key":"14_CR92","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1111\/tgis.12214","volume":"21","author":"N Patel","year":"2017","unstructured":"Patel, N., Stevens, F., Huang, Z., Gaughan, A., Elyazar, I., Tatem, A.: Improving large area population mapping using geotweet densities. Trans. GIS 21(2), 317\u2013331 (2017)","journal-title":"Trans. GIS"},{"issue":"01","key":"14_CR93","first-page":"532","volume":"34","author":"W Yao","year":"2020","unstructured":"Yao, W., Zhang, C., Saravanan, S., Huang, R., Mostafavi, A.: Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management. Proc. AAAI Conf. Artif. Intell. 34(01), 532\u2013539 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"5","key":"14_CR94","doi-asserted-by":"crossref","first-page":"04020068","DOI":"10.1061\/(ASCE)ME.1943-5479.0000836","volume":"36","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Wang, Q., Ji, W.: Rapid assessment of disaster impacts on highways using social media. J. Manag. Eng. 36(5), 04020068 (2020)","journal-title":"J. Manag. Eng."},{"issue":"3","key":"14_CR95","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.giq.2019.04.002","volume":"36","author":"S Harrison","year":"2019","unstructured":"Harrison, S., Johnson, P.: Challenges in the adoption of crisis crowdsourcing and social media in Canadian emergency management. Gov. Inf. Q. 36(3), 501\u2013509 (2019)","journal-title":"Gov. Inf. Q."},{"key":"14_CR96","doi-asserted-by":"crossref","unstructured":"Lv, X., Liao, Y., Deng, L.: Natural disaster emergency rescue system based on the mobile phone\u2019s high-precision positioning. In: 3rd International Conference on Image, Vision and Computing, Chongqing, China. IEEE (2018)","DOI":"10.1109\/ICIVC.2018.8492850"},{"key":"14_CR97","doi-asserted-by":"crossref","unstructured":"Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R.: Prediction of human emergency behavior and their mobility following large-scale disaster. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York (2014)","DOI":"10.1145\/2623330.2623628"},{"issue":"3","key":"14_CR98","doi-asserted-by":"crossref","first-page":"406","DOI":"10.3390\/s16030406","volume":"16","author":"M Romano","year":"2016","unstructured":"Romano, M., Onorati, T., Aedo, I., Diaz, P.: Designing mobile applications for emergency response: citizens acting as human sensors. Sensors 16(3), 406 (2016)","journal-title":"Sensors"},{"key":"14_CR99","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.ijdrr.2017.06.009","volume":"24","author":"M Tan","year":"2017","unstructured":"Tan, M., Prasanna, R., Stock, K., Hudson-Doyle, E., Leonard, G., Johnston, D.: Mobile applications in crisis informatics literature: A systematic review. Int. J. Disaster Risk Reduct. 24, 297\u2013311 (2017)","journal-title":"Int. J. Disaster Risk Reduct."},{"issue":"3","key":"14_CR100","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1007\/s11803-014-0259-4","volume":"13","author":"G Cimellaro","year":"2014","unstructured":"Cimellaro, G., Scura, G., Renschler, C., Reinhorn, A., Kim, H.: Rapid building damage assessment system using mobile phone technology. Earthq. Eng. Eng. Vib. 13(3), 519\u2013533 (2014)","journal-title":"Earthq. Eng. Eng. Vib."},{"key":"14_CR101","doi-asserted-by":"crossref","unstructured":"Salat, H., Smoreda, Z., Schl\u00e4pfer, M.: A method to estimate population densities and electricity consumption from mobile phone data in developing countries. PloS one 15(6), e0235224 (2020)","DOI":"10.1371\/journal.pone.0235224"},{"issue":"45","key":"14_CR102","doi-asserted-by":"crossref","first-page":"15888","DOI":"10.1073\/pnas.1408439111","volume":"111","author":"P Deville","year":"2014","unstructured":"Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F., Gaughan, A., Blondela, V., Tatem, A.: Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. 111(45), 15888\u201315893 (2014)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"14_CR103","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.trc.2019.02.013","volume":"101","author":"D Bachir","year":"2019","unstructured":"Bachir, D., Khodabandelou, G., Gauthier, V., El Yacoubi, M., Puchinger, J.: Inferring dynamic origin-destination flows by transport mode using mobile phone data. Transp. Res. Part C Emerg. Technol. 101, 254\u2013275 (2019)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"14_CR104","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.trc.2019.02.008","volume":"101","author":"H Huang","year":"2019","unstructured":"Huang, H., Cheng, Y., Weibel, R.: Transport mode detection based on mobile phone network data: a systematic review. Transp. Res. Part C Emerg. Technol. 101, 297\u2013312 (2019)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"14_CR105","doi-asserted-by":"crossref","unstructured":"Wilson, R., zu Erbach-Schoenberg, E., Albert, M., Power, D., Tudge, S., Gonzalez, M., Guthrie, S., Chamberlain, H., Brooks, C., Hughes, C., Pitonakova, L., Buckee, C., Lu, X., Wetter, E., Tatem, A., Bengtsson, L.: Rapid and near real-time assessments of population displacement using mobile phone data following disasters: the 2015 Nepal Earthquake. PLoS Curr. 8 (2016)","DOI":"10.1371\/currents.dis.d073fbece328e4c39087bc086d694b5c"},{"issue":"2","key":"14_CR106","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1093\/inthealth\/ihv003","volume":"7","author":"N Bharti","year":"2015","unstructured":"Bharti, N., Lu, X., Bengtsson, L., Wetter, E., Tatem, A.: Remotely measuring populations during a crisis by overlaying two data sources. Int. Health 7(2), 90\u201398 (2015)","journal-title":"Int. Health"},{"key":"14_CR107","doi-asserted-by":"crossref","unstructured":"Pastor-Escuredo, D., Morales-Guzm\u00e1n, A., Torres-Fern\u00e1ndez, Y., Bauer, J., Wadhwa, A., Castro-Correa, C., Romanoff, L., Lee, J., Rutherford, A., Frias-Martinez, V., Oliver, N.: Flooding through the lens of mobile phone activity. In: IEEE Global Humanitarian Technology Conference (GHTC 2014), pp. 279\u2013286. IEEE (2014)","DOI":"10.1109\/GHTC.2014.6970293"},{"key":"14_CR108","doi-asserted-by":"crossref","unstructured":"Ricciato, F., Lanzieri, G., Wirthmann, A., Seynaeve, G.: Towards a methodological framework for estimating present population density from mobile network operator data. Pervasive Mobile Comput. 68, 101263 (2020)","DOI":"10.1016\/j.pmcj.2020.101263"},{"key":"14_CR109","doi-asserted-by":"crossref","unstructured":"Pestre, G., Letouz\u00e9, E., Zagheni, E.: The ABCDE of big data: assessing biases in call-detail records for development estimates. World Bank Econ. Rev. 34(Supplement_1), S89-S97 (2020).","DOI":"10.1093\/wber\/lhz039"},{"issue":"9","key":"14_CR110","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1080\/13658816.2015.1137298","volume":"30","author":"Z Zhao","year":"2016","unstructured":"Zhao, Z., Shaw, S., Xu, Y., Lu, F., Chen, J., Yin, L.: Understanding the bias of call detail records in human mobility research. Int. J. Geogr. Inf. Sci. 30(9), 1738\u20131762 (2016)","journal-title":"Int. J. Geogr. Inf. Sci."},{"issue":"11","key":"14_CR111","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.3390\/w10111536","volume":"10","author":"A Mosavi","year":"2018","unstructured":"Mosavi, A., Ozturk, P., Chau, K.: Flood prediction using machine learning models: literature review. Water 10(11), 1536 (2018)","journal-title":"Water"},{"key":"14_CR112","doi-asserted-by":"crossref","unstructured":"Wagenaar, D., Curran, A., Balbi, M., Bhardwaj, A., Soden, R., Hartato, E., Sarica, G., Ruangpan, L., Molinario, G., Lallemant, D.: Invited perspectives: How machine learning will change flood risk and impact assessment. Nat. Hazards Earth Syst. Sci. 20(4) (2020)","DOI":"10.5194\/nhess-20-1149-2020"},{"key":"14_CR113","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.jhydrol.2018.11.069","volume":"569","author":"Z Yaseen","year":"2019","unstructured":"Yaseen, Z., Sulaiman, S., Deo, R., Chau, K.: An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol. 569, 387\u2013408 (2019)","journal-title":"J. Hydrol."},{"key":"14_CR114","doi-asserted-by":"crossref","unstructured":"Xie, S., Wu, W., Mooser, S., Wang, Q., Nathan, R., Huang, Y.: Artificial neural network based hybrid modeling approach for flood inundation modeling. J. Hydrol. 125605 (2020)","DOI":"10.1016\/j.jhydrol.2020.125605"},{"key":"14_CR115","doi-asserted-by":"crossref","unstructured":"Wu, W., Emerton, R., Duan, Q., Wood, A., Wetterhall, F., Robertson, D.: Ensemble flood forecasting: current status and future opportunities. Wiley Interdiscip. Rev. Water 7(3), e1432 (2020)","DOI":"10.1002\/wat2.1432"},{"issue":"9","key":"14_CR116","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1007\/s11269-020-02603-7","volume":"34","author":"P Yariyan","year":"2020","unstructured":"Yariyan, P., Janizadeh, S., Van Phong, T., Nguyen, H., Costache, R., Van Le, H., Pham, B., Pradhan, B., Tiefenbacher, J.: Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping. Water Resour. Manage 34(9), 3037\u20133053 (2020)","journal-title":"Water Resour. Manage"},{"issue":"9","key":"14_CR117","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.3390\/w10091283","volume":"10","author":"L Chang","year":"2018","unstructured":"Chang, L., Amin, M., Yang, S., Chang, F.: Building ANN-based regional multi-step-ahead flood inundation forecast models. Water 10(9), 1283 (2018)","journal-title":"Water"},{"issue":"2","key":"14_CR118","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3390\/w12020570","volume":"12","author":"A Zanchetta","year":"2020","unstructured":"Zanchetta, A., Coulibaly, P.: Recent advances in real-time pluvial flash flood forecasting. Water 12(2), 570 (2020)","journal-title":"Water"},{"issue":"9","key":"14_CR119","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.5194\/nhess-17-1683-2017","volume":"17","author":"D Wagenaar","year":"2017","unstructured":"Wagenaar, D., Jong, J., Bouwer, L.: Multi-variable flood damage modelling with limited data using supervised learning approaches. Nat. Hazard. 17(9), 1683\u20131696 (2017)","journal-title":"Nat. Hazard."},{"key":"14_CR120","doi-asserted-by":"crossref","unstructured":"Amadio, M., Scorzini, A., Carisi, F., Essenfelder, A., Domeneghetti, A., Mysiak, J., Castellarin, A.: Testing empirical and synthetic flood damage models: the case of Italy. Nat. Hazards Earth Syst. Sci. 19(3) (2019)","DOI":"10.5194\/nhess-19-661-2019"},{"key":"14_CR121","doi-asserted-by":"publisher","DOI":"10.5194\/nhess-2020-220","author":"L Cesarini","year":"2021","unstructured":"Cesarini, L., Figueiredo, R., Monteleone, B., Martina, M.: The potential of machine learning for weather index insurance. Nat. Hazards Earth Syst. Sci. (2021). https:\/\/doi.org\/10.5194\/nhess-2020-220","journal-title":"Nat. Hazards Earth Syst. Sci. "},{"key":"14_CR122","doi-asserted-by":"crossref","unstructured":"Xie, Y., Ebad Sichani, M., Padgett, J., DesRoches, R.: The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthq. Spectra 8755293020919419 (2020)","DOI":"10.1177\/8755293020919419"},{"issue":"2A","key":"14_CR123","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1785\/0220180218","volume":"90","author":"F Khosravikia","year":"2019","unstructured":"Khosravikia, F., Clayton, P., Nagy, Z.: Artificial neural network-based framework for developing ground-motion models for natural and induced earthquakes in Oklahoma, Kansas, and Texas. Seismol. Res. Lett. 90(2A), 604\u2013613 (2019)","journal-title":"Seismol. Res. Lett."},{"key":"14_CR124","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.asoc.2019.03.029","volume":"80","author":"A Derakhshani","year":"2019","unstructured":"Derakhshani, A., Foruzan, A.: Predicting the principal strong ground motion parameters: a deep learning approach. Appl. Soft Comput. 80, 192\u2013201 (2019)","journal-title":"Appl. Soft Comput."},{"key":"14_CR125","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.engstruct.2018.01.008","volume":"160","author":"S Mangalathu","year":"2018","unstructured":"Mangalathu, S., Jeon, J.-S.: Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. Eng. Struct. 160, 85\u201394 (2018)","journal-title":"Eng. Struct."},{"key":"14_CR126","doi-asserted-by":"crossref","unstructured":"Huang, H., Burton, H.: Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning. J. Build. Eng. 25, 100767 (2019)","DOI":"10.1016\/j.jobe.2019.100767"},{"issue":"9","key":"14_CR127","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/mice.12363","volume":"33","author":"Y Gao","year":"2018","unstructured":"Gao, Y., Mosalam, K.: Deep transfer learning for image-based structural damage recognition. Comput. Aided Civil Infrastruct. Eng. 33(9), 748\u2013768 (2018)","journal-title":"Comput. Aided Civil Infrastruct. Eng."},{"key":"14_CR128","doi-asserted-by":"crossref","first-page":"955","DOI":"10.5194\/isprs-archives-XLII-4-W18-955-2019","volume":"42","author":"S Seydi","year":"2019","unstructured":"Seydi, S., Rastiveis, H.: A deep learning framework for roads network damage assessment using post-earthquake LiDAR data. Int. Archives Photogram. Remote Sens. Spat. Inf. Sci. 42, 955\u2013961 (2019)","journal-title":"Int. Archives Photogram. Remote Sens. Spat. Inf. Sci."},{"key":"14_CR129","doi-asserted-by":"crossref","unstructured":"Mangalathu, S., Hwang, S., Choi, E., Jeon, J-S.: Rapid seismic damage evaluation of bridge portfolios using machine learning techniques. Eng. Struct. 201, 109785 (2019)","DOI":"10.1016\/j.engstruct.2019.109785"},{"issue":"2","key":"14_CR130","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s12205-017-1329-8","volume":"22","author":"Z Liu","year":"2018","unstructured":"Liu, Z., Zhang, Z.: Artificial neural network based method for seismic fragility analysis of steel frames. KSCE J. Civ. Eng. 22(2), 708\u2013717 (2018)","journal-title":"KSCE J. Civ. Eng."},{"key":"14_CR131","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1002\/eqe.3183","volume":"48","author":"S Mangalathu","year":"2019","unstructured":"Mangalathu, S., Jeon, J.-S.: Stripe-based fragility analysis of concrete bridge classes using machine learning techniques. Earthq. Eng. Struct. Dynam. 48, 1238\u20132125 (2019)","journal-title":"Earthq. Eng. Struct. Dynam."},{"key":"14_CR132","doi-asserted-by":"crossref","unstructured":"Mangalathu, S., Hwang, S-H., Jeon, J-S.: Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng. Struct. 219, 110927 (2020)","DOI":"10.1016\/j.engstruct.2020.110927"},{"key":"14_CR133","doi-asserted-by":"crossref","unstructured":"Pereira, N., Rom\u00e3o, X.: Damage localization length in RC frame components: mechanical analysis and experimental observations. Eng. Struct. 221, 111026 (2020)","DOI":"10.1016\/j.engstruct.2020.111026"},{"key":"14_CR134","doi-asserted-by":"crossref","unstructured":"Gatti, F., Clouteau, D.: Towards blending Physics-Based numerical simulations and seismic databases using Generative Adversarial Network. Comput. Methods Appl. Mech. Eng. 372, 113421 (2020)","DOI":"10.1016\/j.cma.2020.113421"},{"issue":"4","key":"14_CR135","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","volume":"28","author":"P Jain","year":"2020","unstructured":"Jain, P., Coogan, S., Subramanian, S., Crowley, M., Taylor, S., Flannigan, M.: A review of machine learning applications in wildfire science and management. Environ. Rev. 28(4), 478\u2013505 (2020)","journal-title":"Environ. Rev."},{"key":"14_CR136","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yang, J., He, H.: Identifying the threshold of dominant controls on fire spread in a boreal forest landscape of northeast China. PLoS One 8(1), e55618 (2013)","DOI":"10.1371\/journal.pone.0055618"},{"issue":"7","key":"14_CR137","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1002\/eap.1586","volume":"27","author":"J Lydersen","year":"2017","unstructured":"Lydersen, J., Collins, B., Brooks, M., Matchett, J., Shive, K., Povak, N., Kane, V., Smith, D.: Evidence of fuels management and fire weather influencing fire severity in an extreme fire event. Ecol. Appl. 27(7), 2013\u20132030 (2017)","journal-title":"Ecol. Appl."},{"issue":"11","key":"14_CR138","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1175\/BAMS-D-18-0195.1","volume":"100","author":"A McGovern","year":"2019","unstructured":"McGovern, A., Lagerquist, R., John Gagne, D., Jergensen, G., Elmore, K., Homeyer, C., Smith, T.: Making the black box more transparent: understanding the physical implications of machine learning. Bull. Am. Meteor. Soc. 100(11), 2175\u20132199 (2019)","journal-title":"Bull. Am. Meteor. Soc."},{"key":"14_CR139","unstructured":"Cortez, P., Morais, A: A data mining approach to predict forest fires using meteorological data (2007). Available from https:\/\/repositorium.sdum.uminho.pt\/handle\/1822\/8039"},{"key":"14_CR140","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.firesaf.2019.01.006","volume":"104","author":"Y Sayad","year":"2019","unstructured":"Sayad, Y., Mousannif, H., Al Moatassime, H.: Predictive modeling of wildfires: a new dataset and machine learning approach. Fire Saf. J. 104, 130\u2013146 (2019)","journal-title":"Fire Saf. J."},{"key":"14_CR141","doi-asserted-by":"crossref","first-page":"176746","DOI":"10.1109\/ACCESS.2019.2957837","volume":"7","author":"H Liang","year":"2019","unstructured":"Liang, H., Zhang, M., Wang, H.: A neural network model for wildfire scale prediction using meteorological factors. IEEE Access 7, 176746\u2013176755 (2019)","journal-title":"IEEE Access"},{"key":"14_CR142","doi-asserted-by":"crossref","unstructured":"Michael, Y., Helman, D., Glickman, O., Gabay, D., Brenner, S., Lensky, I.: Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series. Sci. Total Environ. 142844 (2020)","DOI":"10.1016\/j.scitotenv.2020.142844"},{"key":"14_CR143","doi-asserted-by":"crossref","unstructured":"Mohan, A., Singh, A., Kumar, B., Dwivedi, R.: Review on remote sensing methods for landslide detection using machine and deep learning. Trans. Emerg. Telecommun. Technol. e3998 (2020)","DOI":"10.1002\/ett.3998"},{"issue":"2","key":"14_CR144","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3390\/rs11020196","volume":"11","author":"O Ghorbanzadeh","year":"2019","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., Tiede, D., Aryal, J.: Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. 11(2), 196 (2019)","journal-title":"Remote Sens."},{"key":"14_CR145","doi-asserted-by":"crossref","unstructured":"Merghadi, A., Yunus, A., Dou, J., Whiteley, J., ThaiPham, B., Bui, D., Avtar, R., Abderrahmane, B.: Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci. Rev. 103225 (2020)","DOI":"10.1016\/j.earscirev.2020.103225"},{"key":"14_CR146","doi-asserted-by":"crossref","unstructured":"Prakash, N., Manconi, A., Loew, S.: Mapping landslides on EO data: performance of deep learning models vs. traditional machine learning models. Remote Sens. 12(3), 346 (2020)","DOI":"10.3390\/rs12030346"},{"issue":"22","key":"14_CR147","doi-asserted-by":"crossref","first-page":"8189","DOI":"10.3390\/app10228189","volume":"10","author":"S Lee","year":"2020","unstructured":"Lee, S., Baek, W., Jung, H., Lee, S.: Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon. Appl. Sci. 10(22), 8189 (2020)","journal-title":"Appl. Sci."},{"issue":"8","key":"14_CR148","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.3390\/rs10081252","volume":"10","author":"P Kadavi","year":"2018","unstructured":"Kadavi, P., Lee, C., Lee, S.: Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens. 10(8), 1252 (2018)","journal-title":"Remote Sens."},{"issue":"8","key":"14_CR149","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/s10346-020-01392-9","volume":"17","author":"M Di Napoli","year":"2020","unstructured":"Di Napoli, M., Carotenuto, F., Cevasco, A., Confuorto, P., Di Martire, D., Firpo, M., Pepe, G., Raso, E., Calcaterra, D.: Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides 17(8), 1897\u20131914 (2020)","journal-title":"Landslides"},{"issue":"16","key":"14_CR150","doi-asserted-by":"crossref","first-page":"4386","DOI":"10.3390\/su11164386","volume":"11","author":"B Thai Pham","year":"2019","unstructured":"Thai Pham, B., Shirzadi, A., Shahabi, H., Omidvar, E., Singh, S., Sahana, M., Asl, D., Ahmad, B., Quoc, N., Lee, S.: Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability 11(16), 4386 (2019)","journal-title":"Sustainability"},{"key":"14_CR151","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.catena.2018.12.018","volume":"175","author":"B Pham","year":"2019","unstructured":"Pham, B., Prakash, I., Singh, S., Shirzadi, A., Shahabi, H., Bui, D.: Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: hybrid machine learning approaches. CATENA 175, 203\u2013218 (2019)","journal-title":"CATENA"},{"key":"14_CR152","doi-asserted-by":"publisher","DOI":"10.1007\/s10346-020-01513-4","author":"F Catani","year":"2020","unstructured":"Catani, F.: Landslide detection by deep learning of non-nadiral and crowdsourced optical images. Landslides (2020). https:\/\/doi.org\/10.1007\/s10346-020-01513-4","journal-title":"Landslides"},{"issue":"4","key":"14_CR153","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1080\/01431161.2019.1672904","volume":"41","author":"C Zhong","year":"2020","unstructured":"Zhong, C., Liu, Y., Gao, P., Chen, W., Li, H., Hou, Y., Nuremanguli, T., Ma, H.: Landslide mapping with remote sensing: challenges and opportunities. Int. J. Remote Sens. 41(4), 1555\u20131581 (2020)","journal-title":"Int. J. Remote Sens."},{"issue":"11","key":"14_CR154","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.3390\/rs12111737","volume":"12","author":"B Kalantar","year":"2020","unstructured":"Kalantar, B., Ueda, N., Saeidi, V., Ahmadi, K., Halin, A., Shabani, F.: Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data. Remote Sens. 12(11), 1737 (2020)","journal-title":"Remote Sens."},{"issue":"5","key":"14_CR155","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.3390\/s20051425","volume":"20","author":"A van Natijne","year":"2020","unstructured":"van Natijne, A., Lindenbergh, R., Bogaard, T.: Machine learning: new potential for local and regional deep-seated landslide nowcasting. Sensors 20(5), 1425 (2020)","journal-title":"Sensors"},{"issue":"9","key":"14_CR156","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1007\/s10346-020-01453-z","volume":"17","author":"H Thirugnanam","year":"2020","unstructured":"Thirugnanam, H., Ramesh, M., Rangan, V.: Enhancing the reliability of landslide early warning systems by machine learning. Landslides 17(9), 2231\u20132246 (2020)","journal-title":"Landslides"},{"issue":"18","key":"14_CR157","first-page":"1","volume":"13","author":"W Zhang","year":"2020","unstructured":"Zhang, W.: Geological disaster monitoring and early warning system based on big data analysis. Arab. J. Geosci. 13(18), 1\u20139 (2020)","journal-title":"Arab. J. Geosci."},{"key":"14_CR158","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.comcom.2020.02.076","volume":"154","author":"S Karunarathne","year":"2020","unstructured":"Karunarathne, S., Dray, M., Popov, L., Butler, M., Pennington, C., Angelopoulos, C.: A technological framework for data-driven IoT systems: Application on landslide monitoring. Comput. Commun. 154, 298\u2013312 (2020)","journal-title":"Comput. Commun."},{"key":"14_CR159","doi-asserted-by":"crossref","unstructured":"Hong, M., Akerkar, R.: Analytics and evolving landscape of machine learning for emergency response. In: Machine Learning Paradigms, 351\u2013397. Springer, Cham (2019)","DOI":"10.1007\/978-3-030-15628-2_11"},{"key":"14_CR160","doi-asserted-by":"crossref","first-page":"54595","DOI":"10.1109\/ACCESS.2019.2913340","volume":"7","author":"S Shah","year":"2019","unstructured":"Shah, S., Seker, D., Hameed, S., Draheim, D.: The rising role of big data analytics and IoT in disaster management: recent advances, taxonomy and prospects. IEEE Access 7, 54595\u201354614 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"14_CR161","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1080\/0144929X.2019.1610908","volume":"39","author":"F Alam","year":"2020","unstructured":"Alam, F., Ofli, F., Imran, M.: Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria. Behav. Inf. Technol. 39(3), 288\u2013318 (2020)","journal-title":"Behav. Inf. Technol."},{"key":"14_CR162","doi-asserted-by":"publisher","DOI":"10.5194\/nhess-2020-214","author":"A Kruspe","year":"2020","unstructured":"Kruspe, A., Kersten, J., Klan, F.: Detection of informative tweets in crisis events. Nat. Hazards Earth Syst. Sci. Discuss. (2020). https:\/\/doi.org\/10.5194\/nhess-2020-214","journal-title":"Nat. Hazards Earth Syst. Sci. Discuss."},{"key":"14_CR163","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.is.2015.10.010","volume":"57","author":"A Schulz","year":"2016","unstructured":"Schulz, A., Menc\u00eda, E., Schmidt, B.: A rapid-prototyping framework for extracting small-scale incident-related information in microblogs: application of multi-label classification on tweets. Inf. Syst. 57, 88\u2013110 (2016)","journal-title":"Inf. Syst."},{"key":"14_CR164","unstructured":"Liu, W., Shen, X., Wang, H., Tsang, I.: The Emerging Trends of Multi-Label Learning (2020). arXiv preprint arXiv:2011.11197"},{"key":"14_CR165","doi-asserted-by":"crossref","unstructured":"Nizzoli, L., Avvenuti, M., Tesconi, M., Cresci, S.: Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs. Decis. Supp. Syst. 136, 113346 (2020)","DOI":"10.1016\/j.dss.2020.113346"},{"key":"14_CR166","doi-asserted-by":"crossref","unstructured":"Avvenuti, M., Cresci, S., Nizzoli, L., Tesconi, M.: GSP (Geo-Semantic-Parsing): geoparsing and geotagging with machine learning on top of linked data. In: European Semantic Web Conference, pp. 17\u201332. Springer, Cham (2018)","DOI":"10.1007\/978-3-319-93417-4_2"},{"key":"14_CR167","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13634","author":"K Hunt","year":"2020","unstructured":"Hunt, K., Agarwal, P., Zhuang, J.: Monitoring misinformation on Twitter during crisis events: a machine learning approach. Risk Anal. (2020). https:\/\/doi.org\/10.1111\/risa.13634","journal-title":"Risk Anal."},{"key":"14_CR168","doi-asserted-by":"crossref","unstructured":"Faustini, P., Cov\u00f5es, T.: Fake news detection in multiple platforms and languages. Expert Syst. Appl. 113503 (2020)","DOI":"10.1016\/j.eswa.2020.113503"},{"key":"14_CR169","doi-asserted-by":"crossref","unstructured":"Kaufhold, M., Bayer, M., Reuter, C.: Rapid relevance classification of social media posts in disasters and emergencies: A system and evaluation featuring active, incremental and online learning. Inform. Process. Manag. 57(1), 102132 (2020)","DOI":"10.1016\/j.ipm.2019.102132"},{"key":"14_CR170","doi-asserted-by":"crossref","unstructured":"Ofli, F., Imran, M., Alam, F.: Using artificial intelligence and social media for disaster response and management: an overview. AI Rob. Disaster Stud. 63\u201381 (2020)","DOI":"10.1007\/978-981-15-4291-6_5"},{"key":"14_CR171","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., Imran, M., Alam, T., Qazi, U.: Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response (2020). arXiv preprint arXiv:2011.08916","DOI":"10.1109\/ASONAM49781.2020.9381294"},{"issue":"1","key":"14_CR172","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s11069-019-03626-z","volume":"97","author":"X Zhu","year":"2019","unstructured":"Zhu, X., Zhang, G., Sun, B.: A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence. Nat. Hazards 97(1), 65\u201382 (2019)","journal-title":"Nat. Hazards"},{"issue":"3","key":"14_CR173","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1007\/s11069-016-2541-4","volume":"84","author":"M Gul","year":"2016","unstructured":"Gul, M., Guneri, A.: An artificial neural network-based earthquake casualty estimation model for Istanbul city. Nat. Hazards 84(3), 2163\u20132178 (2016)","journal-title":"Nat. Hazards"},{"key":"14_CR174","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s11069-020-03937-6","volume":"102","author":"X Huang","year":"2020","unstructured":"Huang, X., Song, J., Jin, H.: The casualty prediction of earthquake disaster based on Extreme Learning Machine method. Nat. Hazards 102, 873\u2013886 (2020)","journal-title":"Nat. Hazards"},{"issue":"3","key":"14_CR175","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s40747-020-00173-0","volume":"6","author":"Y Feng","year":"2020","unstructured":"Feng, Y., Wang, D., Yin, Y., Li, Z., Hu, Z.: An XGBoost-based casualty prediction method for terrorist attacks. Complex Intell Syst 6(3), 721\u2013740 (2020)","journal-title":"Complex Intell Syst"},{"key":"14_CR176","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s00607-019-00764-x","volume":"102","author":"FA Almalki","year":"2020","unstructured":"Almalki, F.A., Angelides, M.: Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach. Computing 102, 829\u2013864 (2020)","journal-title":"Computing"},{"issue":"2","key":"14_CR177","first-page":"13","volume":"8","author":"H Papadopoulos","year":"2018","unstructured":"Papadopoulos, H., Korakis, A.: Predicting medical resources required to be dispatched after earthquake and flood, using historical data and machine learning techniques: the COncORDE emergency medical service use case. Int. J. Interact. Commun. Syst. Technol. (IJICST) 8(2), 13\u201335 (2018)","journal-title":"Int. J. Interact. Commun. Syst. Technol. (IJICST)"},{"key":"14_CR178","doi-asserted-by":"crossref","unstructured":"Lin, A., Wu, H., Liang, G., Cardenas-Tristan, A., Wu, X., Zhao, C., Li, D.: A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster. Int. J. Disaster Risk Reduct. 101682 (2020)","DOI":"10.1016\/j.ijdrr.2020.101682"},{"issue":"1","key":"14_CR179","first-page":"5","volume":"11","author":"A Nadi","year":"2016","unstructured":"Nadi, A., Edrissi, A.: A reinforcement learning approach for evaluation of real-time disaster relief demand and network condition. Int. J. Econ. Manag. Eng. 11(1), 5\u201310 (2016)","journal-title":"Int. J. Econ. Manag. Eng."},{"key":"14_CR180","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2017.11.026","volume":"205","author":"R Goldblatt","year":"2018","unstructured":"Goldblatt, R., Stuhlmacher, M., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A., Cheng, W., Balling, R., Jr.: Using Landsat and night time lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205, 253\u2013275 (2018)","journal-title":"Remote Sens. Environ."},{"key":"14_CR181","doi-asserted-by":"crossref","unstructured":"Levin, N., Kyba, C., Zhang, Q., de Miguel, A., Rom\u00e1n, M., Li, X., Portnov, B., Molthan, A., Jechow, A., Miller, S., Wang, Z., Shrestha, R., Elvidge, C.: Remote sensing of night lights: a review and an outlook for the future. Remote Sens. Environ. 237, 111443 (2020)","DOI":"10.1016\/j.rse.2019.111443"},{"issue":"10","key":"14_CR182","doi-asserted-by":"crossref","first-page":"737","DOI":"10.14358\/PERS.85.10.737","volume":"85","author":"Y Tan","year":"2019","unstructured":"Tan, Y., Xiong, S., Li, Z., Tian, J., Li, Y.: Accurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network. Photogramm. Eng. Remote. Sens. 85(10), 737\u2013752 (2019)","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"14_CR183","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.neucom.2018.09.106","volume":"396","author":"Y Tan","year":"2020","unstructured":"Tan, Y., Xiong, S., Yan, P.: Multi-branch convolutional neural network for built-up area extraction from remote sensing image. Neurocomputing 396, 358\u2013374 (2020)","journal-title":"Neurocomputing"},{"key":"14_CR184","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","volume":"130","author":"R Alshehhi","year":"2017","unstructured":"Alshehhi, R., Marpu, P., Woon, W., Dalla Mura, M.: Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J. Photogramm. Remote. Sens. 130, 139\u2013149 (2017)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"14_CR185","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.isprsjprs.2020.10.008","volume":"170","author":"Q Chen","year":"2020","unstructured":"Chen, Q., Wang, L., Waslander, S., Liu, X.: An end-to-end shape modeling framework for vectorized building outline generation from aerial images. ISPRS J. Photogramm. Remote. Sens. 170, 114\u2013126 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"14_CR186","unstructured":"Saadi, S., Bensaibi, M.: Detection of buildings height using satellite monoscopic image. In: Second European Conference on Earthquake Engineering and Seismology, Istanbul (2014)"},{"key":"14_CR187","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2017.01.001","volume":"64","author":"F Biljecki","year":"2017","unstructured":"Biljecki, F., Ledoux, H., Stoter, J.: Generating 3D city models without elevation data. Comput. Environ. Urban Syst. 64, 1\u201318 (2017)","journal-title":"Comput. Environ. Urban Syst."},{"key":"14_CR188","doi-asserted-by":"crossref","first-page":"39401","DOI":"10.1109\/ACCESS.2018.2856088","volume":"6","author":"X Gao","year":"2018","unstructured":"Gao, X., Sun, X., Zhang, Y., Yan, M., Xu, G., Sun, H., Jiao, J., Fu, K.: An end-to-end neural network for road extraction from remote sensing imagery by multiple feature pyramid network. IEEE Access 6, 39401\u201339414 (2018)","journal-title":"IEEE Access"},{"issue":"5","key":"14_CR189","doi-asserted-by":"crossref","first-page":"552","DOI":"10.3390\/rs11050552","volume":"11","author":"L Gao","year":"2019","unstructured":"Gao, L., Song, W., Dai, J., Chen, Y.: Road extraction from high-resolution remote sensing imagery using refined deep residual convolutional neural network. Remote Sens. 11(5), 552 (2019)","journal-title":"Remote Sens."},{"issue":"11","key":"14_CR190","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.3390\/rs11111259","volume":"11","author":"E Hoffmann","year":"2019","unstructured":"Hoffmann, E., Wang, Y., Werner, M., Kang, J., Zhu, X.: Model fusion for building type classification from aerial and street view images. Remote Sens. 11(11), 1259 (2019)","journal-title":"Remote Sens."},{"issue":"3","key":"14_CR191","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1111\/mice.12493","volume":"35","author":"A Lenjani","year":"2020","unstructured":"Lenjani, A., Yeum, C., Dyke, S., Bilionis, I.: Automated building image extraction from 360\u00b0 panoramas for postdisaster evaluation. Comput. Aided Civil Infrastruct. Eng. 35(3), 241\u2013257 (2020)","journal-title":"Comput. Aided Civil Infrastruct. Eng."},{"issue":"6","key":"14_CR192","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1080\/13658816.2018.1542698","volume":"34","author":"S Srivastava","year":"2020","unstructured":"Srivastava, S., Vargas Munoz, J., Lobry, S., Tuia, D.: Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data. Int. J. Geogr. Inf. Sci. 34(6), 1117\u20131136 (2020)","journal-title":"Int. J. Geogr. Inf. Sci."},{"issue":"1","key":"14_CR193","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3390\/rs12010109","volume":"12","author":"J G\u00f3mez","year":"2020","unstructured":"G\u00f3mez, J., Pati\u00f1o, J., Duque, J., Passos, S.: Spatiotemporal modeling of urban growth using machine learning. Remote Sens. 12(1), 109 (2020)","journal-title":"Remote Sens."},{"issue":"4","key":"14_CR194","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10661-019-7330-6","volume":"191","author":"M Aburas","year":"2019","unstructured":"Aburas, M., Ahamad, M., Omar, N.: Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review. Environ. Monit. Assess. 191(4), 205 (2019)","journal-title":"Environ. Monit. Assess."},{"issue":"01","key":"14_CR195","first-page":"1","volume":"11","author":"A Aarthi","year":"2019","unstructured":"Aarthi, A., Gnanappazham, L.: Comparison of urban growth modeling using deep belief and neural network based cellular automata model\u2014a case study of Chennai metropolitan area, Tamil Nadu, India. J. Geogr. Inf. Syst. 11(01), 1 (2019)","journal-title":"J. Geogr. Inf. Syst."},{"issue":"10","key":"14_CR196","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1080\/13658816.2019.1600701","volume":"33","author":"T Xu","year":"2019","unstructured":"Xu, T., Gao, J., Coco, G.: Simulation of urban expansion via integrating artificial neural network with Markov chain\u2013cellular automata. Int. J. Geogr. Inf. Sci. 33(10), 1960\u20131983 (2019)","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"14_CR197","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning (2019). arXiv preprint arXiv:1908.09635"},{"key":"14_CR198","unstructured":"Makhlouf, K., Zhioua, S., Palamidessi, C.: On the Applicability of ML Fairness Notions (2020). arXiv preprint arXiv:2006.16745"},{"issue":"2","key":"14_CR199","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s11069-019-03842-7","volume":"100","author":"J Dabbeek","year":"2020","unstructured":"Dabbeek, J., Silva, V.: Modeling the residential building stock in the Middle East for multi-hazard risk assessment. Nat. Hazards 100(2), 781\u2013810 (2020)","journal-title":"Nat. Hazards"},{"key":"14_CR200","doi-asserted-by":"crossref","unstructured":"Soden, R., Kauffman, N.: Infrastructuring the imaginary: how sea-level rise comes to matter in the San Francisco Bay area. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2019)","DOI":"10.1145\/3290605.3300516"},{"issue":"3","key":"14_CR201","doi-asserted-by":"crossref","first-page":"205316801772000","DOI":"10.1177\/2053168017720008","volume":"4","author":"J Mellon","year":"2017","unstructured":"Mellon, J., Prosser, C.: Twitter and Facebook are not representative of the general population: political attitudes and demographics of British social media users. Res. Politics 4(3), 2053168017720008 (2017)","journal-title":"Res. Politics"},{"key":"14_CR202","doi-asserted-by":"crossref","unstructured":"Gambo, S., \u00d6zad, B.: The demographics of computer-mediated communication: a review of social media demographic trends among social networking site giants. Comput. Hum. Behav. Rep. 2, 100016 (2020)","DOI":"10.1016\/j.chbr.2020.100016"},{"key":"14_CR203","doi-asserted-by":"crossref","unstructured":"Fan, C., Esparza, M., Dargin, J., Wu, F., Oztekin, B., Mostafavi, A.: Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters. Comput. Environ. Urban Syst. 83, 101514 (2020)","DOI":"10.1016\/j.compenvurbsys.2020.101514"},{"issue":"6404","key":"14_CR204","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1126\/science.aat5991","volume":"361","author":"M Taddeo","year":"2018","unstructured":"Taddeo, M., Floridi, L.: How AI can be a force for good. Science 361(6404), 751\u2013752 (2018)","journal-title":"Science"},{"key":"14_CR205","doi-asserted-by":"crossref","unstructured":"Toma\u0161ev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Bel-grave, D., Ezer, D., van der Haert, F., Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., de Wever, W., Teh, Y., Khan, M., De Winne, R., Tom Schaul, T., Clopath, C.: AI for social good: unlocking the opportunity for positive impact. Nat. Commun. 11(1), 1\u20136.e (2020)","DOI":"10.1038\/s41467-020-15871-z"}],"container-title":["Structural Integrity","Industry 4.0 for the Built Environment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82430-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T13:23:53Z","timestamp":1638451433000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82430-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,3]]},"ISBN":["9783030824297","9783030824303"],"references-count":205,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82430-3_14","relation":{},"ISSN":["2522-560X","2522-5618"],"issn-type":[{"type":"print","value":"2522-560X"},{"type":"electronic","value":"2522-5618"}],"subject":[],"published":{"date-parts":[[2021,12,3]]},"assertion":[{"value":"3 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}