Mr Abolfazl Abdollahi

Postdoctoral Research Fellow in Remote Sensing and Environment
College of Science

Areas of expertise

  • Artificial Intelligence 4602
  • Earth And Space Science Informatics 370402
  • Environmental Assessment And Monitoring 410402
  • Photogrammetry And Remote Sensing 401304
  • Geospatial Information Systems And Geospatial Data Modelling 401302
  • Natural Hazards 370903
  • Image Processing 460306
  • Machine Learning 4611

Research interests

  • Artificial Intelligence
  • Environmental Monitoring
  • Geospatial Analysis
  • Earth Observation
  • Earth Imagery Processing
  • Time-series Image Analysis
  • Change Detection
  • Bushfire Risk Analysis
  • Risk Modelling and Reduction
  • Natural Hazards
  • GIS Maps Updating
  • Vegetation Mapping
  • Land Cover Mapping
  • Advanced Machine Learning
  • Explainable AI (XAI)

Researcher's projects

Forecasting Grass Pollen with Satellite Sensor Time-series, Meteorology data, and Machine Learning Tools

  • In this project, which was a cross-faculty project between the Faculty of Engineering and Science and Health Department at the University of Technology Sydney (UTS), a consistent set of satellite measures of grass cover and seasonal greenness were investigated to evaluate their utility for predicting airborne grass pollen concentrations based on Artificial Intelligence methods.

Land Cover Classification and GIS Maps Updating from Remote Sensing Data Using Deep Convolutional Neural Networks (DCNNs)

  • Land cover is the detected bio-physical overlay on the Earth’s surface, including materials like grass, forest, pastures, built-up areas, water body, etc. Reliable information on global land cover is required to assist in the solution of a wide range of environmental problems. The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in a range of applications, especially in image processing, land cover classification, and maps updating. Thus, this work aims to deepen the understanding of DCNN models for land use/land cover classification based on time series satellite images in the context of large-scale areas.

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Updated:  02 October 2022 / Responsible Officer:  Director (Research Services Division) / Page Contact:  Researchers