Open Access
Issue
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
Article Number 03022
Number of page(s) 12
Section Large Language Models, Generative AI, and Multimodal Learning
DOI https://doi.org/10.1051/itmconf/20268403022
Published online 06 April 2026
  1. Dixon, T.H., et al., Subsidence and flooding in New Orleans. Nature, 2006. 441(7093): p. 587–588. [Google Scholar]
  2. Meng, X., From structural health monitoring to geo-hazard early warning: An integrated approach using GNSS positioning technology, in Earth Observation of Global Changes (EOGC). 2012, Springer. p. 285–293. [Google Scholar]
  3. Santos, S.M.d., J.J.d.S.P. Cabral, and I.D.d.S. Pontes Filho, Monitoring of soil subsidence in urban and coastal areas due to groundwater overexploitation using GPS. Natural hazards, 2012. 64(1): p. 421–439. [Google Scholar]
  4. Meng, X., et al. Research and development of a pilot project using GNSS and Earth Observation (GeoSHM) for structural health monitoring of the Forth Road Bridge in Scotland. in Proceedings of the Joint International Symposium on Deformation Monitoring, Vienna, Austria. 2016. [Google Scholar]
  5. Celebi, M. and A. Sanli, GPS in pioneering dynamic monitoring of long-period structures. Earthquake Spectra, 2002. 18(1): p. 47–61. [Google Scholar]
  6. Xu, C., et al., Exploring the impacts of speed variances on safety performance of urban elevated expressways using GPS data. Accident Analysis & Prevention, 2019. 123: p. 29–38. [Google Scholar]
  7. Li, P., M. Abdel-Aty, and J. Yuan, Using bus critical driving events as surrogate safety measures for pedestrian and bicycle crashes based on GPS trajectory data. Accident Analysis & Prevention, 2021. 150: p. 105924. [Google Scholar]
  8. Mannering, F.L., V. Shankar, and C.R. Bhat, Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic methods in accident research, 2016. 11: p. 1–16. [Google Scholar]
  9. Guo, F. and Y. Fang, Individual driver risk assessment using naturalistic driving data. Accident Analysis & Prevention, 2013. 61: p. 3–9. [Google Scholar]
  10. Cui, Y. and S.S. Ge, Autonomous vehicle positioning with GPS in urban canyon environments. IEEE transactions on robotics and automation, 2003. 19(1): p. 15–25. [Google Scholar]
  11. Zhong, Z., et al. Integration of GIS/RS/GPS for urban fire response. in 2012 International Conference on Computer Vision in Remote Sensing. 2012. IEEE. [Google Scholar]
  12. Davis, F.D., Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS quarterly, 1989. [Google Scholar]
  13. Liu, R., et al., Integrating entropy‐based naïve Bayes and GIS for spatial evaluation of flood hazard. Risk analysis, 2017. 37(4): p. 756–773. [Google Scholar]
  14. Zhang, Z., et al., Hazard assessment model of ground subsidence coupling AHP, RS and GIS–A case study of Shanghai. Gondwana Research, 2023. 117: p. 344–362. [Google Scholar]
  15. Yang, C., et al., Utilizing cloud computing to address big geospatial data challenges. Computers, environment and urban systems, 2017. 61: p. 120–128. [Google Scholar]
  16. Chang, Y., Y. Wang, and W. Fan, The applied research of embedded gis in the corrosion inspection system of urban underground pipeline, in ICPTT 2009: Advances and Experiences with Pipelines and Trenchless Technology for Water, Sewer, Gas, and Oil Applications. 2009. p. 1680–1688. [Google Scholar]
  17. Shi, W., et al. GIS-based bridge structural health monitoring and management system. in Nondestructive Evaluation and Health Monitoring of Aerospace Materials and Civil Infrastructures. 2002. SPIE. [Google Scholar]
  18. Lu, Q., et al., Moving from building information models to digital twins for operation and maintenance. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 2022. 174(2): p. 46–56. [Google Scholar]
  19. Forkuo, E.K. and J.A. Quaye-Ballard, GIS based fire emergency response system. 2013. [Google Scholar]
  20. Albano, R., et al., A GIS-based model to estimate flood consequences and the degree of accessibility and operability of strategic emergency response structures in urban areas. Natural Hazards and Earth System Sciences, 2014. 14(11): p. 2847–2865. [Google Scholar]
  21. Kankanamge, N., T. Yigitcanlar, and A. Goonetilleke, How engaging are disaster management related social media channels? The case of Australian state emergency organisations. International Journal of Disaster Risk Reduction, 2020. 48: p. 101571. [Google Scholar]
  22. Almeida, C.R.d., A.C. Teodoro, and A. Gonçalves, Study of the urban heat island (UHI) using remote sensing data/techniques: A systematic review. Environments, 2021. 8(10): p. 105. [Google Scholar]
  23. Chuvieco, E., et al., Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological modelling, 2010. 221(1): p. 46–58. [Google Scholar]
  24. Frenelus, W. and H. Peng, Towards long-term monitoring of the structural health of deep rock tunnels with remote sensing techniques. Fracture and Structural Integrity, 2023. 17(66): p. 56–87. [Google Scholar]
  25. Niro, F., et al., European Space Agency (ESA) calibration/validation strategy for optical land-imaging satellites and pathway towards interoperability. Remote Sensing, 2021. 13(15): p. 3003. [Google Scholar]
  26. Cetin, M., et al., Assessing earthquake-induced vulnerability of critical infrastructure in kahramanmaraş using geographic information systems and remote sensing technologies. Journal of the Indian Society of Remote Sensing, 2025. 53(1): p. 169–183. [Google Scholar]
  27. Cooner, A.J., Y. Shao, and J.B. Campbell, Detection of urban damage using remote sensing and machine learning algorithms: Revisiting the 2010 Haiti earthquake. Remote Sensing, 2016. 8(10): p. 868. [Google Scholar]
  28. Mühlbauer, M., et al. Improved satellite-based emergency mapping through automated triggering of processes. in Proceedings of the International ISCRAM Conference. 2024. [Google Scholar]
  29. Baiocchi, V., F. Zottele, and D. Dominici, Remote sensing of urban microclimate change in L’Aquila city (Italy) after post-earthquake depopulation in an open source GIS environment. Sensors, 2017. 17(2): p. 404. [Google Scholar]
  30. Ghaffarian, S., N. Kerle, and T. Filatova, Remote sensing-based proxies for urban disaster risk management and resilience: A review. Remote sensing, 2018. 10(11): p. 1760. [Google Scholar]

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