World's Best Scientists 2026 revealed!
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
9384
National Ranking
84

Omid Ghorbanzadeh publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Omid Ghorbanzadeh sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 67 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Omid Ghorbanzadeh D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Omid Ghorbanzadeh sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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