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  Mr Hadi Yazdi |  Urban tree |  Young Scientist Award

Research associate / PhD candidate at  Technical university of munich, Germany

Hadi Yazdi is a Research Associate and PhD candidate at the Technical University of Munich. He holds a double Master of Science degree from Brandenburg Technical University, Germany, and Poznan University of Technology, Poland. His research focuses on computational methods and machine learning for urban tree canopy growth prediction, with a particular emphasis on data-driven approaches for Urban Green Infrastructure (UGI). His PhD thesis investigates target-driven tree planning and tree species recognition using machine learning. Hadi has published several papers, including work on optimizing tree planting locations and tree crown development in competitive urban settings. He is part of the RTG UGI research group.

Publication profile:

🎓 Academic Background:

Hadi holds a Master of Science in Architecture from Brandenburg Technical University, Germany, and Poznan University of Technology, Poland. His Master’s thesis focused on “Central Courtyard Feature Extraction in Remote Sensing Aerial Images Using Deep Learning”

🌳 Research Focus:

His PhD research explores target-driven tree planting in urban environments, employing computational methods and machine learning to optimize urban green infrastructure (UGI). His innovative work emphasizes tree canopy growth prediction and developing location optimization models for tree planting.

🔬 Key Research Projects:

  • TreeML-Data: A comprehensive dataset on urban tree structure and graph models (Published)
  • TreeML-Species Recognition: Automated classification of tree species using neural networks (Under Review)
  • TreeML-Planter: A 3D model for optimizing tree planting locations using temporal tree crown geometry prediction (Ready for submission)

🔗 Collaborations & Memberships

He is part of the Urban Green Infrastructure Research Group (RTG UGI), collaborating on interdisciplinary projects with 13 PhD candidates and 15 Principal Investigators.

 

Publication Top Notes:

  • Deep learning in historical architecture remote sensing: Automated historical courtyard house recognition in Yazd, Iran
    H. Yazdi, S. Sad Berenji, F. Ludwig, S. Moazen
    Heritage, 5(4), 3066-3080, 2022
  • A target-driven tree planting and maintenance approach for next generation urban green infrastructure (UGI)
    H. Yazdi, Q. Shu, F. Ludwig
    JoDLA – Journal of Digital Landscape Architecture, 178-185, 2023
  • Central courtyard feature extraction in remote sensing aerial images using deep learning: A case-study of Iran
    H. Yazdi, I. Vukorep, M. Banach, S. Moazen, A. Nadolny, R. Starke, …
    Remote Sensing, 13(23), 4843, 2021
  • Daylightophil approach towards high-performance architecture for hybrid-optimization of visual comfort and daylight factor in BSk
    M. Mahdavinejad, H. Yazdi
    International Journal of Architectural and Environmental Engineering, 11(9), 2017
  • A multilayered urban tree dataset of point clouds, quantitative structure and graph models
    H. Yazdi, Q. Shu, T. Rötzer, F. Petzold, F. Ludwig
    Scientific Data, 11(1), 28, 2024
  • The methods of deep learning and big data analysis in promoting sustainable architecture
    H. Yazdi, I. Vukorep, H. Bazazzadeh
    IOP Conference Series: Earth and Environmental Science, 1078(1), 012136, 2022
  • Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning
    Q. Shu, H. Yazdi, T. Rötzer, F. Ludwig
    Frontiers in Plant Science, 15, 1297390, 2024
  • TreeML-Data: A multidisciplinary and multilayer urban tree dataset
    H. Yazdi, Q. Shu, T. Rötzer, F. Petzold, F. Ludwig
    Figshare, 2023
  • Machine learning-based prediction of tree crown development in competitive urban environments
    H. Yazdi, A. Moser-Reischl, T. Rötzer, F. Petzold, F. Ludwig
    Urban Forestry & Urban Greening, 128527, 2024
  • GroTree: A novel toolbox for simulating and managing urban tree canopy growth
    H. Yazdi, Q. Shu, X. Chen, T. Rötzer, F. Ludwig
    Journal of Digital Landscape Architecture, 815-825, 2024
  • A systems perspective on the interactions between urban green infrastructure and the built environment
    R. Reitberger, N. Pattnaik, L. Parhizgar, C. Trost, H. Yazdi, M. A. Rahman, …
    IOP Conference Series: Earth and Environmental Science, 1363(1), 012071, 2024
  • Urban green infrastructure for resilient urban transformations: A system dynamics modelling approach for streets as multifunctional spaces
    J. Micklewright, M. Baghaie Poor, E. Fakirova, H. Yazdi, M. A. Rahman
    Proceedings of 37th PLEA Conference, Sustainable Architecture and Urban Design, 2024
  • Digital workflow for novel urban green system design derived from a historical role model
    F. Ludwig, M. Hensel, T. Rötzer, A. Ahmeti, X. Chen, H. I. Erdal, A. Reischel, …
    2024
  • Results brochure of the research training group Urban Green Infrastructure – Training next generation professionals for integrated urban planning research
    S. Pauleit, I. Alim, M. Baghaie Poor, F. Banihashemi, N. Berger, M. Egerer, …
    2024
  • The effect of improper interfering in the historical architecture on energy wasting (Case study: Bibi-Roqayyeh House, Yazd, Iran)
    H. Yazdi, G. Mügge, S. Movafagh, M. H. Fallahi, F. Ludwig, S. Moazen
    IOP Conference Series: Earth and Environmental Science, 1196(1), 012103, 2023
  • Can leaf area density be estimated from quantitative structure models of trees?
    Q. Shu, T. Rötzer, H. Yazdi, A. Moser-Reischl, F. Ludwig
    Available at SSRN, 4855810, 2024

 

 

Hadi Yazdi | Urban tree | Young Scientist Award

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