Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making
Publication: Journal of Management in Engineering
Volume 36, Issue 3
Abstract
The benefits of a digital twin city have been assessed based on real-time data collected from preinstalled Internet of Things (IoT) sensors (e.g., traffic, energy use, air pollution, water quality) for managing the complex systems of cities, but the sensor-based reality information is likely insufficient to provide dynamic spatiotemporal information about physical vulnerabilities. Understanding cities’ current states of physical vulnerability can support city decision makers in analyzing associated potential risk in urban areas for data-driven infrastructure management in extreme weather events. As a step toward creating a digital twin city for effective risk-informed decision-making, this paper proposes a new framework to bring crowdsourced visual data-based reality information into a three-dimensional (3D) virtual city for a model update with interactive and immersive visualization. Unstructured visual data are collected from participatory sensing and analyzed to estimate the geospatial information of vulnerable objects in the distance representing physical vulnerability in cities. The crowdsourced visual data–based reality information of physical vulnerability in a given region is then integrated with a 3D virtual city model, and the updated 3D city model is fed into a computer-aided virtual environment (CAVE) for immersive visualization to enable users to navigate the intersection of reality and virtuality. To test the proposed framework, case studies were conducted on Houston. The outcomes demonstrate that the proposed method has the potential to make the virtual city model live in terms of local vulnerability. The digital twin city building on crowdsourced visual data is expected to contribute to risk-informed decision-making for infrastructure management in cities and help analyze various what-if scenarios in disaster situations with increased visibility of hazard and city interactions.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, and code generated or used during the study are available from the corresponding author by request, including GPS and compass bearing data during experiments.
Acknowledgments
This material is in part based on work supported by the National Science Foundation (NSF) under Civil, Mechanical and Manufacturing Innovation Award #1832187. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
References
Al-Amin, A. Q., G. J. Nagy, M. M. Masud, W. L. Filho, and B. Doberstein. 2019. “Evaluating the impacts of climate disasters and the integration of adaptive flood risk management.” Int. J. Disaster Risk Reduct. 39 (Oct): 101241. https://doi.org/10.1016/j.ijdrr.2019.101241.
Biljecki, F., J. Stoter, H. Ledoux, S. Zlatanova, A. Çöltekin, F. Biljecki, J. Stoter, H. Ledoux, S. Zlatanova, and A. Çöltekin. 2015. “Applications of 3D city models: State of the art review.” ISPRS Int. J. Geo-Inf. 4 (4): 2842–2889. https://doi.org/10.3390/ijgi4042842.
Chen, H., B. Guo, Z. Yu, and Q. Han. 2016. “Toward real-time and cooperative mobile visual sensing and sharing.” In Proc., IEEE INFOCOM 2016—35th Annual IEEE Int. Conf. on Computer Communications, 1–9. New York: IEEE.
Cheng, L., Y. Yuan, N. Xia, S. Chen, Y. Chen, K. Yang, L. Ma, and M. Li. 2018. “Crowd-sourced pictures geo-localization method based on street view images and 3D reconstruction.” ISPRS J. Photogramm. Remote Sens. 141 (Jul): 72–85. https://doi.org/10.1016/j.isprsjprs.2018.04.006.
Cheng, Y. 1995. “Mean shift, mode seeking, and clustering.” IEEE Trans. Pattern Anal. Mach. Intell. 17 (8): 790–799. https://doi.org/10.1109/34.400568.
Christin, D., A. Reinhardt, S. S. Kanhere, and M. Hollick. 2011. “A survey on privacy in mobile participatory sensing applications.” J. Syst. Software 84 (11): 1928–1946. https://doi.org/10.1016/j.jss.2011.06.073.
Coletta, C., L. Evans, L. Heaphy, and R. Kitchin. 2019. Creating smart cities. London: Routledge.
Cooper, R. 2018. “Urban planning in 3D: How creating a digital twin leads to smarter cities.” Accessed September 20, 2018. https://meetingoftheminds.org/urban-planning-3d-creating-digital-twin-leads-smarter-cities-25212.
Dai, J., Y. Li, K. He, and J. Sun. 2016. “R-FCN: Object detection via region-based fully convolutional networks.” In Proc., 30th Int. Conf. on Neural Information Processing Systems, 379–387. San Diego: Neural Information Processing Systems Foundation.
Dashti, S., L. Palen, M. P. Heris, K. M. Anderson, T. J. Anderson, and S. Anderson. 2014. “Supporting disaster reconnaissance with social media data: A design-oriented case study of the 2013 Colorado floods.” In Proc., 11th Int. ISCRAM Conf. State College, PA: Information Systems for Crisis Response and Management, University Park.
Dassault Systèmes. 2018. “Meet virtual Singapore, the city’s 3D digital twin.” Accessed September 20, 2018. https://govinsider.asia/digital-gov/meet-virtual-singapore-citys-3d-digital-twin/.
Eilander, D., P. Trambauer, J. Wagemaker, and A. van Loenen. 2016. “Harvesting social media for generation of near real-time flood maps.” Procedia Eng. 154: 176–183. https://doi.org/10.1016/j.proeng.2016.07.441.
Fohringer, J., D. Dransch, H. Kreibich, and K. Schröter. 2015. “Social media as an information source for rapid flood inundation mapping.” Nat. Hazards Earth Syst. Sci. 15 (12): 2725–2738. https://doi.org/10.5194/nhess-15-2725-2015.
Geddie, J., and A. Aravindan. 2018. “Virtual Singapore project could be test bed for planners—and plotters.” Accessed February 9, 2019. https://www.reuters.com/article/us-singapore-technology/virtual-singapore-project-could-be-test-bed-for-planners-and-plotters-idUSKCN1M70U1.
Geodan. 2018. “Geodan launches the digital twin of Amsterdam Southeast.” Accessed January 17, 2020. https://www.geodan.com/knowledge-and-innovation/managing-urban-processes-intelligently-with-the-amsterdam-smart-city-dashboard/.
Goldman, J., K. Shilton, J. Burke, D. Estrin, M. Hansen, N. Ramanathan, S. Reddy, M. S. Samanta, M. Srivastava, and R. West. 2009. Participatory sensing: A citizen-powered approach to illuminating the patterns that shape our world. Washington, DC: Woodrow Wilson International Center for Scholars.
Ha, I., H. Kim, S. Park, and H. Kim. 2018. “Image retrieval using BIM and features from pretrained VGG network for indoor localization.” Build. Environ. 140 (Aug): 23–31. https://doi.org/10.1016/j.buildenv.2018.05.026.
Ham, Y., and H. Yoon. 2018. “Motion and visual data-driven distant object localization for field reporting.” J. Comput. Civ. Eng. 32 (4): 04018020. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000767.
He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep residual learning for image recognition.” In Proc., 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 770–778. New York: IEEE.
Holstein, W. J. 2018. “Virtual Singapore-Creating an intelligent 3D model to improve experiences of residents, business and government.” Accessed November 20, 2018. https://compassmag.3ds.com/8/Cover-Story/VIRTUAL-SINGAPORE.
Interagency Security Committee. 2015. “Presidential policy directive 21 implementation: An interagency security committee white paper.” Accessed November 13, 2018. https://www.dhs.gov/sites/default/files/publications/ISC-PPD-21-Implementation-White-Paper-2015-508.pdf.
Jiang, H., P. Lin, and M. Qiang. 2016. “Public-opinion sentiment analysis for large hydro projects.” J. Constr. Eng. Manage. 142 (2): 05015013. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001039.
Jongman, B., J. Wagemaker, B. Romero, and E. de Perez. 2015. “Early flood detection for rapid humanitarian response: Harnessing near real-time satellite and twitter signals.” ISPRS Int. J. Geo-Inf. 4 (4): 2246–2266. https://doi.org/10.3390/ijgi4042246.
Kirilenko, A. P., T. Molodtsova, and S. O. Stepchenkova. 2015. “People as sensors: Mass media and local temperature influence climate change discussion on Twitter.” Global Environ. Change 30 (Jan): 92–100. https://doi.org/10.1016/j.gloenvcha.2014.11.003.
Kirilenko, A. P., and S. O. Stepchenkova. 2014. “Public microblogging on climate change: One year of Twitter worldwide.” Global Environ. Change 26 (May): 171–182. https://doi.org/10.1016/j.gloenvcha.2014.02.008.
Manweiler, J. G., P. Jain, and R. Roy Choudhury. 2012. “Satellites in our pockets.” In Proc., 10th Int. Conf. on Mobile Systems, Applications, and Services—MobiSys ’12, 211. New York: ACM Press.
Mohammadi, N., and J. E. Taylor. 2017. “Smart city digital twins.” In Proc., 2017 IEEE Symp. Series on Computational Intelligence, SSCI 2017, 1–5. Piscataway, NJ: Institute of Electrical and Electronics Engineers.
Muralidharan, S., L. Rasmussen, D. Patterson, and J.-H. Shin. 2011. “Hope for Haiti: An analysis of Facebook and Twitter usage during the earthquake relief efforts.” Public Relations Rev. 37 (2): 175–177. https://doi.org/10.1016/j.pubrev.2011.01.010.
Najafizadeh, L., and J. E. Froehlich. 2018. “A feasibility study of using Google street view and computer vision to track the evolution of urban accessibility.” In Proc., 20th Int. ACM SIGACCESS Conf. on Computers and Accessibility—ASSETS ’18, 340–342. New York: ACM Press.
National Centers for Environmental Information. 2018. “Magnetic field calculators.” Accessed February 11, 2019. https://www.ngdc.noaa.gov/geomag/calculators/magcalc.shtml#declination.
National Infrastructure Commission. 2017. “Data for the public good.” Accessed February 4, 2019. https://www.nic.org.uk/publications/data-public-good/.
OpenStreetMap. 2019. “OpenStreetMap.” Accessed May 27, 2019. https://www.openstreetmap.org/.
Oud, D. 2018. “Geodan, Amsterdam and Johan Cruijff Arena organized livable smart cities by design.” Accessed January 17, 2020. https://www.geodan.com/news/the-livable-smart-city/.
Ouyang, R. W., A. Srivastava, P. Prabahar, R. Roy Choudhury, M. Addicott, and F. J. McClernon. 2013. “If you see something, swipe towards it: Crowdsourced event localization using smartphones.” In Proc., 2013 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing—UbiComp ’13, 23. New York: ACM Press.
Piasco, N., D. Sidibé, C. Demonceaux, and V. Gouet-Brunet. 2018. “A survey on visual-based localization: On the benefit of heterogeneous data.” Pattern Recognit. 74 (Feb): 90–109. https://doi.org/10.1016/j.patcog.2017.09.013.
Qin, T., H. Ma, D. Zhao, T. Li, and J. Chen. 2015. Crowdsourcing based event reporting system using smartphones with accurate localization and photo tamper detection, 141–151. Cham, Switzerland: Springer.
Rehak, D., P. Senovsky, M. Hromada, T. Lovecek, and P. Novotny. 2018. “Cascading impact assessment in a critical infrastructure system.” Int. J. Crit. Infrastruct. Prot. 22 (Sep): 125–138. https://doi.org/10.1016/j.ijcip.2018.06.004.
Restrepo-Estrada, C., S. C. de Andrade, N. Abe, M. C. Fava, E. M. Mendiondo, and J. P. de Albuquerque. 2018. “Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring.” Comput. Geosci. 111 (Feb): 148–158. https://doi.org/10.1016/j.cageo.2017.10.010.
Serre, D. 2010. Matrices: Theory and applications. New York: Springer.
Sun, D., S. Li, W. Zheng, A. Croitoru, A. Stefanidis, and M. Goldberg. 2016. “Mapping floods due to Hurricane Sandy using NPP VIIRS and ATMS data and geotagged Flickr imagery.” Int. J. Digital Earth 9 (5): 427–441. https://doi.org/10.1080/17538947.2015.1040474.
Taeby, M., and L. Zhang. 2019. “Exploring stakeholder views on disaster resilience practices of residential communities in South Florida.” Nat. Hazards Rev. 20 (1): 04018028. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000319.
Tsujikawa, N., S. Tsuchida, and T. Shiotani. 2016. “Changes in the factors influencing public acceptance of nuclear power generation in Japan since the 2011 Fukushima Daiichi nuclear disaster.” Risk Anal. 36 (1): 98–113. https://doi.org/10.1111/risa.12447.
United Nations. 2014. “World urbanization prospects: The 2014 revision.” Accessed May 17, 2019. https://population.un.org/wup/Publications/Files/WUP2014-Methodology.pdf.
United States Geological Survey. 2019. “National elevation dataset (NED).” Accessed May 27, 2019. https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map.
Valentin, V., N. Naderpajouh, and D. Abraham. 2018. “Impact of characteristics of infrastructure projects on public opinion.” J. Manage. Eng. 34 (1): 04017051. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000576.
Virtual Singapore. 2018. “Virtual Singapore.” Accessed September 20, 2018. https://www.nrf.gov.sg/programmes/virtual-singapore.
Wang, R. Q., H. Mao, Y. Wang, C. Rae, and W. Shaw. 2018. “Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data.” Comput. Geosci. 111 (Feb): 139–147. https://doi.org/10.1016/j.cageo.2017.11.008.
White, T. 2018. “Newcastle’s ‘digital twin’ to help city plan for disasters.” Accessed January 31, 2019. https://www.theguardian.com/cities/2018/dec/30/newcastles-digital-twin-to-help-city-plan-for-disasters.
Xue, Y., Z. Ju, K. Xiang, J. Chen, and H. Liu. 2018. “Multimodal human hand motion sensing and analysis-a review.” In Proc., IEEE Transactions on Cognitive and Developmental Systems. New York: IEEE.
Zhang, C., C. Fan, W. Yao, X. Hu, and A. Mostafavi. 2019. “Social media for intelligent public information and warning in disasters: An interdisciplinary review.” Int. J. Inf. Manage. 49 (Dec): 190–207. https://doi.org/10.1016/j.ijinfomgt.2019.04.004.
Information & Authors
Information
Published In
Journal of Management in Engineering
Volume 36 • Issue 3 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Feb 20, 2019
Accepted: Sep 19, 2019
Published online: Feb 5, 2020
Published in print: May 1, 2020
Discussion open until: Jul 5, 2020
ASCE Technical Topics:
- Business management
- Decision making
- Engineering fundamentals
- Infrastructure
- Infrastructure vulnerability
- Measurement (by type)
- Models (by type)
- Municipal water
- Physical models
- Practice and Profession
- Sensors and sensing
- Three-dimensional models
- Urban and regional development
- Urban areas
- Water (by type)
- Water and water resources
- Water management
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.