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Jagannath Aryal
Jagannath Aryal
Associate Professor, Department of Infrastructure Engineering, The University of Melbourne
Verifisert e-postadresse på unimelb.edu.au - Startside
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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
7752019
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
5532007
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
2342007
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
2052021
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
1892019
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
1682019
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
1572017
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
1462019
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
1442016
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
1312018
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
1282017
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
1272020
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
1132019
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
982019
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
972016
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
852020
A Review on Drone-Based Data Solutions for Cereal Crops
US Panday, AK Pratihast, J Aryal, RB Kayastha
drones 4 (41), 1-29, 2020
842020
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
792019
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
782015
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
722014
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Artikler 1–20