Jagannath Aryal (0000-0002-4875-2127)

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Biography

I am an Associate Professor at the Department of Infrastructure Engineering of the University of Melbourne, Melbourne, Australia. My research interests are Earth Observation (EO), Spatial Statistics and Spatial Analysis, Geoinformation retrieval, GEographic Object-Based Image Analysis (GEOBIA), and Modern Land Administration Solutions.

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The University of Melbourne: Melbourne, VIC, AU

2023-01-01 to present | Associate Professor (Infrastructure Engineering)

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Jagannath Aryal

https://orcid.org/0000-0002-4875-2127

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Biography

Activities

Employment (5)

The University of Melbourne: Melbourne, VIC, AU

The University of Melbourne: Melbourne, VIC, AU

University of Tasmania: Hobart, TAS, AU

University of Tasmania: Hobart, TAS, AU

CNRS UMR 7033: Avignon, PACA, FR

Education and qualifications (3)

Lincoln University, New Zealand: Christchurch, Canterbury, NZ

University of Otago: Dunedin, NZ

University of Twente: Enschede, Overijssel, NL

Professional activities (2)

Kathmandu University: Kathmandu, NP

University of Cambridge: Cambridge, Cambridge, GB

Works (50 of 140)

Multilabel Learning With ViT for Building Footprint Extraction From Off-Nadir Aerial Images

A novel architecture for automated delineation of the agricultural fields using partial training data in remote sensing images

Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations?

Benchmarking Deep Learning Architectures for Urban Vegetation Point Cloud Semantic Segmentation From MLS

GreenSegNet: A Novel Deep Learning Architecture for Urban Vegetation Segmentation From MLS Data

CNNs for remote extraction of urban features: A survey-driven benchmarking

A Novel Multiscale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification

Characterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia

Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis

Dynamic Wind Effect on Fireline Intensity in Rugged Terrain

Gell River Fire Driven by Forced Channeling and Lateral Fire in 2018–19 Summer, Tasmania

Riveaux Road Fire Driven by Dynamic Winds in Tasmania, Australia

Applying Bayesian Models to Reduce Computational Requirements of Wildfire Sensitivity Analyses

Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction

Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

Energy and latency aware mobile task assignment for green cloudlets

Impact of Vertical Atmospheric Structure on an Atypical Fire in a Mountain Valley

Quantitative contribution of the Grain for Green Program to vegetation greening and its spatiotemporal variation across the Chinese Loess Plateau

Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia

Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network

Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia

Quantitative contribution of the Grain for Green project to vegetation greening and its spatiotemporal variation across the Chinese Loess Plateau

Container Orchestration in Edge and Fog Computing Environments for Real-Time IoT Applications

A probability-based risk metric for operational wildfire risk management

Multivariate singular spectrum analysis of seismicity in the space–time-depth-magnitude domain: insight from eastern Nepal and the southern Tibetan Himalaya

NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Spectral Retrieval of Eucalypt Leaf Biochemical Traits by Inversion of the Fluspect-Cx Model

Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy

A Surrogate Model for Rapidly Assessing the Size of a Wildfire over Time

Characterizing Spatial and Temporal Variability of Lightning Activity Associated with Wildfire over Tasmania, Australia

Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

Comparison of changes in vegetation and land cover types between Shenzhen and Bangkok

Investigating the price of the New Zealand wool clip using modelling approaches

Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi‑collinearity analysis and K-fold cross-validation

A Review on Drone-Based Data Solutions for Cereal Crops

A cloud-based framework for sensitivity analysis of natural hazard models

A mixed-methods study of the demographic and behavioural correlates of walking to a more distant bus stop

Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal

Using innovations to understand tourist mobility in national parks

A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change

Cloud Computing in natural hazard modeling systems: Current research trends and future directions

Comparing techniques for tracking: The case of tourism tracer in Tasmania, Australia

Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection

Leveraging machine learning to extend Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA): A case study in forest-type mapping

Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest

Using tracking technology to improve marketing: insights from a historic town in Tasmania, Australia

Role of geospatial technology in understanding urban green space of Kalaburagi city for sustainable planning

MAPPING URBAN TREES WITHIN CADASTRAL PARCELS USING AN OBJECT-BASED CONVOLUTIONAL NEURAL NETWORK

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

Peer review (3 reviews for 3 publications/grants)