| 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 | 780 | 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 | 556 | 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 | 234 | 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 | 208 | 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 | 191 | 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 | 170 | 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 | 158 | 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 | 148 | 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 | 144 | 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 | 132 | 2018 |
| 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 | 130 | 2017 |
| 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 | 127 | 2020 |
| 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 | 114 | 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 | 98 | 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 | 98 | 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 | 90 | 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 | 85 | 2020 |
| 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 | 79 | 2019 |
| 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 | 78 | 2015 |
| 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 | 72 | 2014 |
Thomas BlaschkeUniversity of Salzburg Department of GeoinformaticsEmail yang diverifikasi di sbg.ac.at
Omid GhorbanzadehUniversity of Natural Resources and Life Sciences (BOKU)Email yang diverifikasi di boku.ac.at
Ritaban DuttaCSIROEmail yang diverifikasi di csiro.au
Bipul NeupaneThe University of MelbourneEmail yang diverifikasi di student.unimelb.edu.au
Renaud MathieuSenior Scientist, International Rice Research InstituteEmail yang diverifikasi di irri.org
Ahsan MorshedCQUnivesityEmail yang diverifikasi di cqu.edu.au
Arko LucieerUniversity of TasmaniaEmail yang diverifikasi di utas.edu.au
Paul Fox-HughesPrincipal Research Scientist, Bureau of MeteorologyEmail yang diverifikasi di bom.gov.au
Dirk TiedeUniversity of Salzburg Department of GeoinformaticsEmail yang diverifikasi di plus.ac.at
Professor Biswajeet Pradhan, SMIEEE...Distinguished Prof. & Director, CAMGIS Center, University of Technology Sydney, AustraliaEmail yang diverifikasi di uts.edu.au
claire freemanUniversity of OtagoEmail yang diverifikasi di geography.otago.ac.nzArun Kumar Pratihast, PhDSr. Data Scientist, Wageningen Environmental Research, Wageningen University & Research, WageningenEmail yang diverifikasi di wur.nl
Uma Shankar PandayKathmandu UniversityEmail yang diverifikasi di ku.edu.np
Hejar ShahabiPhD Candidate of Environmental Remote Sensing, INRS, QuebecEmail yang diverifikasi di inrs.ca
Bethany MelvilleUniversity of TasmaniaEmail yang diverifikasi di utas.edu.au
Jamie B. KirkpatrickSchool of Geography, Planning and Spatial Sciences, University of TasmaniaEmail yang diverifikasi di utas.edu.au
Bakhtiar FeizizadehUniversity of TabrizEmail yang diverifikasi di tabrizu.ac.ir
Sepideh Tavakkoli PiralilouPhD condidate, University of SalzburgEmail yang diverifikasi di stud.sbg.ac.at
Malgorzata O'ReillyUniversity of TasmaniaEmail yang diverifikasi di utas.edu.au
