| 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 | 740 | 2019 |
| Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery R Mathieu, C Freeman, J Aryal Landscape and urban planning 81 (3), 179-192, 2007 | 546 | 2007 |
| Object-based classification of Ikonos imagery for mapping large-scale vegetation communities in urban areas R Mathieu, J Aryal, AK Chong Sensors 7 (11), 2860-2880, 2007 | 231 | 2007 |
| Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis B Neupane, T Horanont, J Aryal Remote Sensing 13 (4), 808, 2021 | 191 | 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 | 177 | 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 | 159 | 2019 |
| Tracking tourists’ travel with smartphone-based GPS technology: a methodological discussion A Hardy, S Hyslop, K Booth, B Robards, J Aryal, U Gretzel, R Eccleston Information Technology & Tourism 17, 255-274, 2017 | 150 | 2017 |
| Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches O Ghorbanzadeh, VK Kamran, T Blaschke, J Aryal, A Naboureh, J Einali, ... Fire 2 (43), 1-23, 2019 | 142 | 2019 |
| Big data integration shows Australian bush-fire frequency is increasing significantly R Dutta, A Das, J Aryal Royal Society open science 3 (2), 150241, 2016 | 140 | 2016 |
| 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 O Ghorbanzadeh, H Rostamzadeh, T Blaschke, K Gholaminia, J Aryal Natural Hazards 94, 497-517, 2018 | 129 | 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 |
| Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping B Feizizadeh, MS Roodposhti, T Blaschke, J Aryal Arabian Journal of Geosciences 10, 1-13, 2017 | 123 | 2017 |
| 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 | 109 | 2019 |
| Cloud Computing in natural hazard modeling systems: Current research trends and future directions U K.C., S Garg, J Hilton, J Aryal, N Forbes-Smith International Journal of Disaster Risk Reduction, 2019 | 97 | 2019 |
| Fuzzy shannon entropy: A hybrid gis-based landslide susceptibility mapping method M Shadman Roodposhti, J Aryal, H Shahabi, T Safarrad Entropy 18 (10), 343, 2016 | 94 | 2016 |
| A Review on Drone-Based Data Solutions for Cereal Crops US Panday, AK Pratihast, J Aryal, RB Kayastha drones 4 (41), 1-29, 2020 | 80 | 2020 |
| Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN) S Timilsina, J Aryal, JB Kirkpatrick Remote Sensing 12 (18), 1-27, 2020 | 78 | 2020 |
| A geospatial approach to assessing soil erosion in a watershed by integrating socio-economic determinants and the RUSLE model KP Bhandari, J Aryal, R Darnsawasdi Natural Hazards 75, 321-342, 2015 | 77 | 2015 |
| A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change MS Roodposhti, J Aryal, BA Bryan Environmental Modelling & Software 112, 70-81, 2019 | 76 | 2019 |
| Development of an intelligent environmental knowledge system for sustainable agricultural decision support R Dutta, A Morshed, J Aryal, C D'Este, A Das Environmental Modelling & Software 52, 264-272, 2014 | 74 | 2014 |
Thomas BlaschkeUniversity of Salzburg Department of Geoinformaticssbg.ac.at의 이메일 확인됨
