| Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection O Ghorbanzadeh, T Blaschke, K Gholamnia, SR Meena, D Tiede, J Aryal Remote Sensing 11 (2), 196, 2019 | 752 | 2019 |
| Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory TG Nachappa, ST Piralilou, K Gholamnia, O Ghorbanzadeh, O Rahmati, ... Journal of hydrology 590, 125275, 2020 | 314 | 2020 |
| Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran PTT Ngo, M Panahi, K Khosravi, O Ghorbanzadeh, N Kariminejad, ... Geoscience Frontiers 12 (2), 505-519, 2021 | 306 | 2021 |
| Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables O Ghorbanzadeh, T Blaschke, K Gholamnia, J Aryal Fire 2 (3), 50, 2019 | 180 | 2019 |
| Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas S Tavakkoli Piralilou, H Shahabi, B Jarihani, O Ghorbanzadeh, ... Remote Sensing 11 (21), 2575, 2019 | 162 | 2019 |
| Sustainable urban transport planning considering different stakeholder groups by an interval-AHP decision support model O Ghorbanzadeh, S Moslem, T Blaschke, S Duleba Sustainability 11 (1), 9, 2018 | 162 | 2018 |
| Analysing stakeholder consensus for a sustainable transport development decision by the fuzzy AHP and interval AHP S Moslem, O Ghorbanzadeh, T Blaschke, S Duleba Sustainability 11 (12), 3271, 2019 | 151 | 2019 |
| Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches O Ghorbanzadeh, K Valizadeh Kamran, T Blaschke, J Aryal, A Naboureh, ... Fire 2 (3), 43, 2019 | 147 | 2019 |
| Landslide detection using deep learning and object-based image analysis O Ghorbanzadeh, H Shahabi, A Crivellari, S Homayouni, T Blaschke, ... Landslides 19 (4), 929-939, 2022 | 142 | 2022 |
| A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold crossvalidation approach for land subsidence susceptibility mapping O Ghorbanzadeh, H Rostamzadeh, T Blaschke, K Gholaminia, J Aryal Natural Hazards, 2018 | 131 | 2018 |
| A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping O Ghorbanzadeh, T Blaschke, J Aryal, K Gholaminia Journal of Spatial Science 65 (3), 401-418, 2020 | 126 | 2020 |
| Comparisons of diverse machine learning approaches for wildfire susceptibility mapping K Gholamnia, T Gudiyangada Nachappa, O Ghorbanzadeh, T Blaschke Symmetry 12 (4), 604, 2020 | 123 | 2020 |
| Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses O Ghorbanzadeh, B Feizizadeh, T Blaschke Geomatics, Natural Hazards and Risk 9 (1), 127-151, 2018 | 121 | 2018 |
| Flood susceptibility mapping using an improved analytic network process with statistical models P Yariyan, M Avand, RA Abbaspour, A Torabi Haghighi, R Costache, ... Geomatics, Natural Hazards and Risk 11 (1), 2282-2314, 2020 | 120 | 2020 |
| DEM resolution effects on machine learning performance for flood probability mapping M Avand, A Kuriqi, M Khazaei, O Ghorbanzadeh Journal of Hydro-Environment Research 40, 1-16, 2022 | 111 | 2022 |
| UAV-based slope failure detection using deep-learning convolutional neural networks O Ghorbanzadeh, SR Meena, T Blaschke, J Aryal Remote Sensing 11 (17), 2046, 2019 | 111 | 2019 |
| 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) O Ghorbanzadeh, A Crivellari, P Ghamisi, H Shahabi, T Blaschke Scientific Reports 11 (1), 14629, 2021 | 106 | 2021 |
| Mapping potential nature-based tourism areas by applying GIS-decision making systems in East Azerbaijan Province, Iran O Ghorbanzadeh, S Pourmoradian, T Blaschke, B Feizizadeh Journal of Ecotourism 18 (3), 261-283, 2019 | 105 | 2019 |
| Decision tree based ensemble machine learning approaches for landslide susceptibility mapping A Arabameri, S Chandra Pal, F Rezaie, R Chakrabortty, A Saha, ... Geocarto International 37 (16), 4594-4627, 2021 | 104 | 2021 |
| Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection O Ghorbanzadeh, Y Xu, P Ghamisi, M Kopp, D Kreil arXiv preprint arXiv:2206.00515, 2022 | 102 | 2022 |
Thomas BlaschkeUniversity of Salzburg Department of GeoinformaticsVerified email at sbg.ac.at
