Hasti Shwan

Hasti Shwan is a professional engineer registered with Engineers and Geoscientists British Columbia (EGBC) and Engineers Geoscientists Manitoba (EGM), with over16 years of experience in civil and environmental engineering, geospatial applications, and mapping services. As a consultant in Geomount Inc. and Reycove Engineering, he applies AI and multisensory data (LiDAR, thermal and GPR) to enhance stormwater and drainage system modelling. A certified Transport Canada Drone Pilot and Flight Reviewer, he leads projects that integrate spatial analytics, digital automation, and infrastructure resilience.

At the University of Waterloo’s Centre for Pavement and Transportation Technology (CPATT), under the supervision of Prof. Pejoohan Tavassoti, his Ph.D. research focuses on deep learning and geospatial modelling to assess climate impacts on pavement performance—specifically the effects of water ponding, surface drainage, and runoff behaviour under extreme weather scenarios. He is also a Ph.D. Fellow in the Master of Business, Entrepreneurship and Technology (MBET) program at the Conrad School of Entrepreneurship and Business, integrating engineering innovation with business strategy and technology commercialization.

Hasti is an active member of the Geospatial Intelligence and Mapping (GIM) Lab and serves as a Senior Engineer, leading the development of a digital flood-risk and infrastructure resilience platform to support municipalities and transportation agencies.

• Ph.D. in Civil Engineering, University of Waterloo,  2023 - Ongoing 
• M.Sc. in Water Resources Engineering, University of Sulaimani, 2013 – 2016
• B.Sc. in Civil Engineering, University of Sulaimani, 2006 – 2010

• H. Shwan and P. Tavassoti, “Assessing Climate Impacts on Pavement Performance: Scenario-Based Analysis of Rainfall, Runoff, and Infiltration for Urban Resilience,” Journal of Water Management Modeling (JWMM), [submitted], 2025.

• Hasti Shwan and Pejoohan Tavassoti. 2025. Integrating UAV-based LiDAR and imaging data for semi-automated detection of water ponding in pavement networks. Canadian Journal of Civil Engineering. 52(9): 1732-1742. [Published] https://doi.org/10.1139/cjce-2024-0539

• S. Abdalla, H. Shwan, J. Li, S. Samuel, and A. Singh, “UAV-Based Spectral Analysis for Predicting Street Lighting Efficiency,” IEEE Transactions on Intelligent Vehicles, [submitted], 2024.
• H. Abdullah, M. Al-Khafaji, and H. Ibrahim, “Assessment of water clarity within Dokan Lake using remote sensing techniques,” Journal of Engineering, vol. 23, no. 8, pp. 13–28, 2018. [Published]. Available Link 
• H. Abdullah, M. Al-Khafaji, and H. Ibrahim, “Water quality assessment models for Dokan Lake using Landsat 8 OLI satellite images,” Journal of Zankoy Sulaimani Part A – Pure and Applied Sciences, vol. 19, no. 3–4(A), pp. 25–44, 2017. [Published]. Available: https://doi.org/10.17656/jzs.10630.

• H. Shwan, P. Tavassoti, and P. Steelman, “An Integrated Multisensory Approach for Non-Destructive Assessment of Surface and Subsurface Pavement Defects,” in Proc. Canadian Society for Civil Engineering (CSCE) Annual Conf. 2026, Springer, Winnipeg, MB pp. GC-613-(1-10), 2025.
• H. Shwan, “Advanced Predictive Analytics for Climate-Resilient Road Infrastructure Using Multisensory and Deep Learning,” presented at the 1st Hojan Science Forum (Yekemîn Foruma Zanistî ya Hojan), University of Ottawa, 2025.
• H. Shwan and P. Tavassoti, “Assessing Climate Impacts on Pavement Performance: Scenario-Based Analysis of Rainfall, Runoff, and Infiltration for Urban Resilience,” presented at the 58th International Conference on Water Management Modeling (ICWMM), Toronto, Canada, 2025.
• H. Abdullah, “Water quality assessment using remote sensing & GIS,” presented at the Society for Conservation GIS Annual Conference (SCGIS 2017), Pacific Grove, California, USA, 2017.
• H. Abdullah, “Water quality index models for Dokan Lake using Landsat 8 OLI satellite images,” presented at the IEEE Young Professionals Conf. on Remote Sensing, Oberpfaffenhofen, Germany, 2016.

• Abdullah, H. (2016). Water quality assessment for Dokan Lake using Landsat 8 OLI Satellite images. MSc Thesis, University of Sulaimani. https://doi.org/10.13140/RG.2.2.11499.03360 (Published)