Dr Abolfazl Abdollahi

Contacts

Postdoctoral Researcher

Dr Abolfazl Abdollahi is a research scientist in the field of Artificial Intelligence (AI), Earth Observation, and Remote Sensing. He completed his Ph.D. at the School of Civil and Environmental Engineering, University of Technology Sydney (UTS). Embarking on a journey through the frontiers of Artificial Intelligence (AI), his Ph.D. endeavor centered on its applications within Earth observation and Remote Sensing. Abolfazl's research encompassed developing conventional machine learning and deep learning models including CNNs, Deep CNNs, and Generative AI to effectively process various remote sensing products, track vegetation changes, update GIS maps, and analyze dynamic landscapes over time. Additionally, he delved into the realm of Explainable AI (XAI) to guarantee transparency and nurture trust and understanding in AI models.

Abolfazl holds the position of postdoctoral research fellow at the Bushfire Research Centre of Excellence, Australian National University (ANU). In this capacity, he is actively engaged as a project manager in the Bushfire Data Challenges program, which is part of the Australian Research Data Common (ARDC's) Translational Research Data Challenges initiative. The Bushfire project develops innovative digital infrastructure solutions to current data challenges in bushfire research with the aim of improving Australia's bushfire resilience, response, and recovery. In collaboration with a list of partners such as TERN, CSIRO, AFAC, and ARDC who are actively involved in the project, the aim is to create aggregated and harmonized datasets for bushfire fuel attributes known to influence the fire behavior processes on a national scale (Australia) using Earth observation techniques and Artificial Intelligence (AI). The project also involves an examination of the spatial distribution of fuels within diverse vegetation and fuel types and an investigation of their response to climate change and bushfire disturbance.

Abolfazl has demonstrated remarkable accomplishments, with a prolific record of publishing numerous scientific papers in esteemed international journals. He also serves as a regular reviewer and academic editor for top-tier journals in his field. Furthermore, he has successfully obtained several competitive grants, awards, paid internships, and scholarships, underscoring the excellence and significance of his research contributions. Throughout his academic journey, he has collaborated with world-renowned experts and worked on interdisciplinary projects. This collaboration has significantly contributed to his knowledge, expertise, and experience in the field.

He has taught undergraduate and graduate courses, such as "Environmental Sensing, Mapping and Modelling", "Risk Assessment and Management", and "Introduction to Information Systems". Additionally, he supervises the research of honours and graduate students in a range of remote sensing and AI topics.

Research interests

    • Artificial Intelligence
    • Remote Sensing
    • Environmental Monitoring
    • Bushfire
    • Earth Observation
    • Time-series Image Analysis
    • Change Detection
    • Risk Modelling and Reduction
    • Natural Hazards
    • GIS Maps Updating
    • Vegetation Dynamics
    • Land Cover Analysis
    • Advanced Machine Learning
    • Explainable AI (XAI)
    • Fuel Attributes Dynamics and Spatial Mapping

    Projects

    • Abdollahi, A & Yebra, M 2024, 'Harnessing Earth observation data and machine learning for national biomass assessment in Australia', Fire Behavior and Fuels Conference, Canberra, Australia, 15-19 April, 2024. https://doi.org/10.5281/zenodo.10807396
    • Abdollahi, A & Yebra, M 2023, 'Remote sensing and machine learning techniques for above-ground biomass estimation on a regional scale
      ', 25th International Congress on Modelling and Simulation (MODSIM2023), ed. Vaze, J., Chilcott, C., Hutley, L. and Cuddy, S.M, Modelling and Simulation Society of Australia and New Zealand Inc., Australia.
    • Abdollahi, A & Pradhan, B 2023, 'Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model', Science of the Total Environment, vol. 879, p. 14. https://doi.org/10.1016/j.scitotenv.2023.163004.
    • Abdollahi, A. & Yebra, M. 2023, 'Forest fuel type classification: Review of remote sensing techniques, constraints and future trends
      ', Journal of Environmental Management, 342, p.118315. https://doi.org/10.1016/j.jenvman.2023.118315.
    • Abdollahi, A, Liu, Y, Pradhan, B, Huete, A et al. 2022, 'Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture
      ', The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 3, pp. 673-685. https://doi.org/10.1016/j.ejrs.2022.06.002.
    • Abdollahi, A & Pradhan, B 2021, 'Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI)
      ', Sensors, vol. 21, no. 14, p. 16. https://doi.org/10.3390/s21144738.
    • Abdollahi, A, Pradhan, B & Alamri, A 2022, 'SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data
      ', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, p. 15. https://doi.org/10.1109/TGRS.2022.3143855.
    • Abdollahi, A, Pradhan, B, Shukla, N et al. 2021, 'Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data
      ', Remote Sensing, vol. 13, no. 18, p. 22. https://doi.org/10.3390/rs13183710.

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