Towards a digital twin for smart resilient cities: real-time fire and smoke tracking and prediction platform for community awareness (FireCom)

Abstract

This paper discusses the development and application of a digital twin (DT) for urban resilience, focusing on an integrated platform for real-time fire and smoke. The proposed platform, FireCom, adapts DT concepts for the unique challenges of urban fire management, which differ significantly from regional wildfire systems. Through an exploratory case study in Austin, Texas, in the United States, this research bridges the theoretical foundations of 3D DT with their practical application in fire and smoke management. By fusing diverse data sources, ranging from air quality sensors and meteorological data to 3D urban infrastructure, FireCom supports both emergency response and public awareness through a publicly accessible dashboard. Unlike platforms developed primarily for wildland fire applications, FireCom is specifically designed to account for urban complexities such as building canyon effects on smoke dispersion and the heightened exposure risks associated with dense populations. This study contributes a scalable, replicable framework for municipalities seeking data-driven tools for proactive disaster management, with implications for broader climate resilience planning in urban areas.

Keywords

3D digital twin / Smart city / Urban fire / Smoke prediction / Data aggregation

References

[1] AirNow. (n.d.). AirNow current air quality map. Retrieved October 13, 2023, from https://gispub.epa.gov/airnow/?monitors=ozonepm&contours=none&panel=0

[2] Arya SPAir pollution meteorology and dispersion, 1999Oxford University Press310

[3] Austin Fire Department. (n.d.). Active fire incident page. Retrieved October 13, 2023, from https://services.austintexas.gov/fact/default.cfm

[4] Bi, T., Wang, P., & Zhang, Q. (2018). Design and implementation of digital fire control system based on BIM and 3DGIS. 1810–1813. https://doi.org/10.2991/ifeesm-17.2018.327

[5] Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Pulido, C. L., Akhavan, N., Bartos, M., Passalacqua, P., & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 178:1015630.

[6] Briggs GA. Diffusion estimation for small emissions. Atmospheric Turbulence and Diffusion Laboratory, 1973, 965: 83-145

[7] Cardil A, Monedero S, Silva CA, Ramirez J. Adjusting the rate of spread of fire simulations in real-time. Ecological Modelling, 2019, 395: 39-44.

[8] CDC. (2021). PLACES: Local data for better health. https://www.cdc.gov/places/

[9] U.S. Census Bureau. (n.d.). 2015–2019 ACS 5-year estimates census tract data. Retrieved October 13, 2023, from https://data.census.gov/

[10] Chesnokova, S. (2025). “Wildfire digital twin” of the Earth: OroraTech grabs €37M to scale satellite-powered fire intelligence — TFN. Tech Funding News. https://techfundingnews.com/wildfire-digital-twin-of-the-earth-ororatech-grabs-e37m-to-scale-satellite-powered-fire-intelligence/

[11] City of Austin. (n.d.). City of Austin open data portal. Retrieved October 13, 2023, from https://data.austintexas.gov/

[12] El Saddik A. Digital twins: The convergence of multimedia technologies. IEEE Multimedia, 2018, 25(2): 87-92.

[13] Fakhruddin B, Kirsch-Wood J, Niyogi D, Guoqing L, Murray V, Frolova N. Harnessing risk-informed data for disaster and climate resilience. Progress in Disaster Science, 2022, 16. 100254

[14] FCAMMS. (n.d.). VSmoke-Web. Fire consortia for advanced modeling of meteorology and smoke. Retrieved October 13, 2023, from https://weather.gfc.state.ga.us/GoogleVsmoke/vsmoke-Good2.html

[15] Cal Fire. (2025). Palisades fire. https://shorturl.at/TwU0H

[16] Fuller A, Fan Z, Day C, Barlow C. Digital twin: Enabling technologies, challenges and open research. IEEE Access, 2020, 8: 108952-108971.

[17] Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, 1818.

[18] Gong H, Su D, Zeng S, Chen X. Parallel simulation and prediction techniques for digital twins in urban underground spaces. Automation in Construction, 2025, 175. 106212

[19] Gubala, T. (n.d.). BlenderPy script. Retrieved October 13, 2023, from https://github.com/TylerGubala/blenderpy

[20] Hall, S., & Evarts, B. (2022). Fire loss in the United States during 2021. National Fire Protection Association.

[21] Horsley JA, Broome RA, Johnston FH, Cope M, Morgan GG. Health burden associated with fire smoke in Sydney, 2001–2013. The Medical Journal of Australia, 2018, 208(7): 309-310.

[22] Huang Y, Li J, Zheng H. Modeling of wildfire digital twin: Research progress in detection, simulation, and prediction techniques. Fire, 2024, 7(11. 412

[23] Isikdag U, Underwood J, Aouad G. An investigation into the applicability of building information models in geospatial environment in support of site selection and fire response management processes. Advanced Engineering Informatics, 2008, 22(4): 504-519.

[24] Jeong D-Y, Baek M-S, Lim T-B, Kim Y-W, Kim S-H, Lee Y-T, Jung W-S, Lee I-B. Digital twin: Technology evolution stages and implementation layers with technology elements. IEEE Access, 2022, 10: 52609-52620.

[25] Kaginalkar, A., Kumar, S., Gargava, P., Kharkar, N., & Niyogi, D. (2022). SmartAirQ: A big data governance framework for urban air quality management in smart cities. Frontiers in Environmental Science, 228:785129.

[26] Kaginalkar A, Kumar S, Gargava P, Niyogi D. Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective. Urban Climate, 2021, 39. 100972

[27] Kamath, H. G., Singh, M., Magruder, L. A., Yang, Z.L., & Niyogi, D. (2022). GLOBUS: GLObal building heights for Urban Studies. arXiv Preprint arXiv:2205.12224.

[28] Kamath HG, Singh M, Malviya N, Martilli A, He L, Aliaga D, He C, Chen F, Magruder LA, Yang Z-L. GLObal building heights for urban studies (UT-GLOBUS) for city-and street-scale urban simulations: Development and first applications. Scientific Data, 2024, 11(1. 886

[29] Kaur, M. J., Mishra, V. P., & Maheshwari, P. (2020). The convergence of digital twin, IoT, and machine learning: Transforming data into action. In M. Farsi, A. Daneshkhah, A. Hosseinian-Far, & H. Jahankhani (Eds.), Digital twin technologies and smart cities (pp. 3–17). Springer International Publishing. https://doi.org/10.1007/978-3-030-18732-3_1

[30] Kim H, Kwon JW, Yun S, Kim WT. A novel wildfire digital-twin framework using interactive wildfire spread simulator. Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), 2019, 2019: 636-638.

[31] Lavdas LG. Improving control of smoke from prescribed fire using low visibility occurrence risk index. Southern Journal of Applied Forestry, 1996, 20(1): 10-14.

[32] Lavdas, L. G. (1996b). Program VSMOKE-users manual. Southern Research Station.

[33] Lewis RH, Jiao J, Seong K, Farahi A, Navrátil P, Casebeer N, Niyogi D. Fire and smoke digital twin – A computational framework for modeling fire incident outcomes. Computers, Environment and Urban Systems, 2024, 110. 102093

[34] Li J, Hu M. Mapping spatial inequity in urban fire service provision: A Moran’s I analysis of station pressure distribution. ISPRS International Journal of Geo-Information, 2025, 14(4. 164

[35] Li X, Liu H, Wang W, Zheng Y, Lv H, Lv Z. Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning. Future Generation Computer Systems, 2022, 128: 167-177.

[36] Li Y, Zhang T, Ding Y, Wadhwani R, Huang X. Review and perspectives of digital twin systems for wildland fire management. Journal of Forestry Research, 2024, 36(1. 14

[37] Lochhead I, Hedley N. Mixed reality emergency management: Bringing virtual evacuation simulations into real-world built environments. International Journal of Digital Earth, 2019, 12(2): 190-208.

[38] Mallia DV, Kochanski AK, Kelly KE, Whitaker R, Xing W, Mitchell LE, Jacques A, Farguell A, Mandel J, Gaillardon P-E. Evaluating wildfire smoke transport within a coupled fire-atmosphere model using a high-density observation network for an episodic smoke event along Utah’s Wasatch Front. Journal of Geophysical Research: Atmospheres, 2020, 125(20. e2020JD032712

[39] Mandalapu A, Seong K, Jiao J. Evaluating urban fire vulnerability and accessibility to fire stations and hospitals in Austin, Texas. PLoS Climate, 2024, 3(7. e0000448

[40] Mendi, A. F. (2022). A digital twin case study on automotive production line. Sensors. https://doi.org/10.3390/s22186963

[41] Mendi, A. F., Erol, T., & Doğan, D. (2022). Digital Twin in the Military Field. IEEE Internet Computing, 26(5), 33–40. https://doi.org/10.1109/MIC.2021.3055153. IEEE Internet Computing

[42] Mohammadi, N., & Taylor, J. E. (2017). Smart city digital twins. pp.1–5.

[43] MSN. (n.d.). Microsoft air quality map. Retrieved October 13, 2023, from https://www.msn.com/en-in/weather/maps/airquality/in-undefined?zoom=15

[44] National Weather Service. (n.d.). NWS Austin/San Antonio. Retrieved October 13, 2023, from https://www.weather.gov/ewx/

[45] Neirotti P, De Marco A, Cagliano AC, Mangano G, Scorrano F. Current trends in smart city initiatives: Some stylised facts. Cities, 2014, 38: 25-36.

[46] Nguyen TH, Hung NT, Nagashima T, Lam YF, Doan Q-V, Kurokawa J, Chatani S, Derdouri A, Cheewaphongphan P, Khan A. Development of current and future high-resolution gridded emission inventory of anthropogenic air pollutants for urban air quality studies in Hanoi, Vietnam. Urban Climate, 2022, 46. 101334

[47] NOAA Research. (n.d.). The High-Resolution Rapid Refresh (HRRR). Retrieved October 13, 2023, from rapidrefresh.noaa.gov/hrrr

[48] Occhipinti C, Aneja VP, Showers W, Niyogi D. Back-trajectory analysis and source-receptor relationships: Particulate matter and nitrogen isotopic composition in rainwater. Journal of the Air & Waste Management Association, 2008, 58(9): 1215-1222.

[49] OpenTopography. (n.d.). OpenTopography. Retrieved October 13, 2023, from https://opentopography.org/

[50] Over M, Schilling A, Neubauer S, Zipf A. Generating web-based 3D city models from OpenStreetMap: The current situation in Germany. Computers, Environment and Urban Systems, 2010, 34(6): 496-507.

[51] Pan, H., Dou, Z., Cai, Y., Li, W., Lei, X., & Han, D. (2020). Digital twin and its application in power system. 2020 5th International Conference on Power and Renewable Energy (ICPRE), 21–26. https://doi.org/10.1109/ICPRE51194.2020.9233278

[52] PurpleAir. (n.d.). PurpleAir real-time air quality monitoring. Retrieved October 13, 2023, from https://map.purpleair.com/

[53] Razavi, H., Titidezh, O., Asgary, A., & Bonakdari, H. (2024). Building resilient smart cities: The role of digital twins and generative AI in disaster management strategy. In S. Pourroostaei Ardakani & A. Cheshmehzangi (Eds.), Digital twin computing for urban intelligence (pp. 95–118). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-8483-7_5

[54] Sadrabadi MT, Peiró J, Innocente MS, Rein G. Conceptual design of a wildfire emergency response system empowered by swarms of unmanned aerial vehicles. International Journal of Disaster Risk Reduction, 2025.

[55] NASA Science. (2024). "Wildfire digital twin” pioneers new AI models and streaming data techniques for forecasting fire and smoke. https://science.nasa.gov/science-research/science-enabling-technology/nasa-wildfire-digital-twin-pioneers-new-ai-models-and-streaming-data-techniques-for-forecasting-fire-and-smoke/

[56] Shie RH, Chan CC. Tracking hazardous air pollutants from a refinery fire by applying on-line and off-line air monitoring and back trajectory modeling. Journal of Hazardous Materials, 2013, 261: 72-82.

[57] Shuman, J. K., Balch, J. K., Barnes, R. T., Higuera, P. E., Roos, C. I., Schwilk, D. W., Stavros, E. N., Banerjee, T., Bela, M. M., Bendix, J., Bertolino, S., Bililign, S., Bladon, K. D., Brando, P., Breidenthal, R. E., Buma, B., Calhoun, D., Carvalho, L. M. V., Cattau, M. E., … Zhang, X. (2022). Reimagine fire science for the anthropocene. PNAS Nexus,1(3), pgac115. https://doi.org/10.1093/pnasnexus/pgac115

[58] Tashakkori H, Rajabifard A, Kalantari M. A new 3D indoor/outdoor spatial model for indoor emergency response facilitation. Building and Environment, 2015, 89: 170-182.

[59] Turner, D. B. (2020). Workbook of atmospheric dispersion estimates: An introduction to dispersion modeling, second edition (2nd ed.). CRC Press. https://doi.org/10.1201/9780138733704

[60] Vrabič R, Erkoyuncu JA, Butala P, Roy R. Digital twins: Understanding the added value of integrated models for through-life engineering services. Procedia Manufacturing, 2018, 16: 139-146.

[61] Walker, B., Holling, C. S., Carpenter, S. R., & Kinzig, A. (2004). Resilience, adaptability and transformability in social–ecological systems. Ecology and Society,9(2). https://doi.org/10.5751/ES-00650-090205

[62] White G, Zink A, Codecá L, Clarke S. A digital twin smart city for citizen feedback. Cities, 2021, 110. 103064

[63] Yan J, Lu Q, Li N, Chen L, Pitt M. Common data environment for digital twins from building to city levels. Automation in Construction, 2025, 174. 106131

[64] Yan J, Lu Q, Li N, Pitt M. Developing data requirements for city-level digital twins: Stakeholder perspective. Journal of Management in Engineering, 2025, 41(2. 04024068

[65] Yu D, He Z. Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: Advances, challenges, and opportunities. Natural Hazards, 2022, 112(1): 1-36.

[66] Zheng, X., Petrali, P., Lu, J., Turrin, C., & Kiritsis, D. (2022). RMPFQ: A quality-oriented knowledge modelling method for manufacturing systems towards cognitive digital twins. Frontiers in Manufacturing Technology,2. https://doi.org/10.3389/fmtec.2022.901364

[67] Zheng Y, Yang S, Cheng H. An application framework of digital twin and its case study. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1141-1153.

[68] Zohdi TI. A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments. Computer Methods in Applied Mechanics and Engineering, 2020, 363. 112907

[69] Zohdi TI. A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety. Computer Methods in Applied Mechanics and Engineering, 2021, 373. 113446

Funding

The University of Texas at Austin(Bridging Barriers Initiative Good Systems Grand Challenge)

City of Austin(UTA19-000382)

National Science Foundation(2043060)

National Institute of Standards and Technology(60NANB24D235)

CSE-OCE(1835739)

RIGHTS & PERMISSIONS

The Author(s)