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Omid Ghorbanzadeh

Omid Ghorbanzadeh

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
43
Citations
5081
World Ranking
545
National Ranking
1

Computer Science

D-Index
40
Citations
5897
World Ranking
9385
National Ranking
84

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Omid Ghorbanzadeh is affiliated with the University of Salzburg in Austria and conducts research primarily in the fields of Environmental Science and Engineering. Their work focuses on several subfields, including Global and Planetary Change, Management, Monitoring, Policy and Law, Environmental Engineering, Media Technology, and Safety, Risk, Reliability and Quality.

The main research topics addressed by Omid Ghorbanzadeh include:

  • Landslides and related hazards
  • Flood Risk Assessment and Management
  • Fire effects on ecosystems
  • Remote-Sensing Image Classification
  • Hydrology and Watershed Management Studies
  • Hydrology and Drought Analysis
  • Groundwater and Watershed Analysis

Omid Ghorbanzadeh has contributed to various publications, with recent notable papers including:

  • "Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran," 2020, Geoscience Frontiers
  • "Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory," 2020, Journal of Hydrology
  • "Landslide detection using deep learning and object-based image analysis," 2022, Landslides
  • "Flood susceptibility mapping using an improved analytic network process with statistical models," 2020, Geomatics Natural Hazards and Risk
  • "A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)," 2021, Scientific Reports

The frequent co-authors collaborating with Omid Ghorbanzadeh include:

  • Thomas Blaschke (23 publications)
  • Pedram Ghamisi (19 publications)
  • Hejar Shahabi (8 publications)
  • Khalil Gholamnia (7 publications)
  • Thimmaiah Gudiyangada Nachappa (6 publications)

Omid Ghorbanzadeh's works are regularly published in well-known venues such as:

  • Remote Sensing (8 publications)
  • Geomatics Natural Hazards and Risk (5 publications)
  • Symmetry (3 publications)
  • ISPRS International Journal of Geo-Information (3 publications)
  • Landslides (2 publications)

The research of Omid Ghorbanzadeh broadly covers the use of advanced machine learning, deep learning, and analytical methods for environmental risk assessment focusing on landslides and floods. The integration of remote sensing data and image classification techniques is a consistent theme throughout their work. This composite expertise spans both theoretical approaches and practical applications for hazard mapping and management.

Best Publications

  • Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

    Omid Ghorbanzadeh;Thomas Blaschke;Khalil Gholamnia;Sansar Raj Meena

  • Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

    Phuong Thao Thi Ngo;Mahdi Panahi;Khabat Khosravi;Omid Ghorbanzadeh

  • Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory

    Thimmaiah Gudiyangada Nachappa;Sepideh Tavakkoli Piralilou;Khalil Gholamnia;Omid Ghorbanzadeh

  • Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches

    O Ghorbanzadeh;K Valizadeh Kamran;T Blaschke;J Aryal

  • Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas

    Sepideh Tavakkoli Piralilou;Hejar Shahabi;Ben Jarihani;Ben Jarihani;Omid Ghorbanzadeh

  • Sustainable Urban Transport Planning Considering Different Stakeholder Groups by an Interval-AHP Decision Support Model

    Omid Ghorbanzadeh;Sarbast Moslem;Thomas Blaschke;Szabolcs Duleba

  • A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan).

    Omid Ghorbanzadeh;Alessandro Crivellari;Pedram Ghamisi;Hejar Shahabi

  • Analysing Stakeholder Consensus for a Sustainable Transport Development Decision by the Fuzzy AHP and Interval AHP

    Sarbast Moslem;Omid Ghorbanzadeh;Thomas Blaschke;Szabolcs Duleba

  • Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

    Khalil Gholamnia;Thimmaiah Gudiyangada Nachappa;Omid Ghorbanzadeh;Thomas Blaschke

  • DEM resolution effects on machine learning performance for flood probability mapping

    Mohammadtaghi Avand;Alban Kuriqi;Majid Khazaei;Omid Ghorbanzadeh

  • Decision Tree based ensemble machine learning approaches for landslide susceptibility mapping

    Alireza Arabameri;Subodh Chandra Pal;Fatemeh Rezaie;Rabin Chakrabortty

  • A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

    Omid Ghorbanzadeh;Hashem Rostamzadeh;Thomas Blaschke;Khalil Gholaminia

  • UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks

    Omid Ghorbanzadeh;Sansar Raj Meena;Thomas Blaschke;Jagannath Aryal

  • Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach

    Sansar Raj Meena;Sansar Raj Meena;Omid Ghorbanzadeh;Cees J. van Westen;Thimmaiah Gudiyangada Nachappa

  • Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses

    Omid Ghorbanzadeh;Bakhtiar Feizizadeh;Thomas Blaschke

  • A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

    Omid Ghorbanzadeh;Thomas Blaschke;Jagannath Aryal;Khalil Gholaminia

  • Earthquake Vulnerability Mapping Using Different Hybrid Models

    Peyman Yariyan;Mohammadtaghi Avand;Fariba Soltani;Omid Ghorbanzadeh

  • The application of ResU-net and OBIA for landslide detection from multi-temporal sentinel-2 images

    Unknown

  • Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria

    Thimmaiah Gudiyangada Nachappa;Omid Ghorbanzadeh;Khalil Gholamnia;Thomas Blaschke

  • An integrated approach of best-worst method (bwm) and triangular fuzzy sets for evaluating driver behavior factors related to road safety

    Sarbast Moslem;Muhammet Gul;Danish Farooq;Erkan Celik

  • An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping

    Omid Ghorbanzadeh;Bakhtiar Feizizadeh;Thomas Blaschke

  • A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia.

    Hejar Shahabi;Ben Jarihani;Ben Jarihani;Sepideh Tavakkoli Piralilou;David Chittleborough;David Chittleborough

Frequent Co-Authors

Thomas Blaschke
Thomas Blaschke University of Salzburg
Dirk Tiede
Dirk Tiede University of Salzburg
David J. Chittleborough
David J. Chittleborough University of Adelaide
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
Pedram Ghamisi
Pedram Ghamisi Helmholtz-Zentrum Dresden-Rossendorf
Omid Rahmati
Omid Rahmati Agricultural Research Education And Extention Organization
Ramesh P. Singh
Ramesh P. Singh Chapman University
Rabin Chakrabortty
Rabin Chakrabortty Asian Institute of Technology
M. Santosh
M. Santosh China University of Geosciences
Artemi Cerdà
Artemi Cerdà University of Valencia

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