World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
95
Citations
43520
World Ranking
455
National Ranking
28

Environmental Sciences

D-Index
95
Citations
44360
World Ranking
468
National Ranking
35

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2013 - IEEE Fellow For contributions to the remote sensing of land cover

Overview

Giles M. Foody is affiliated with the University of Nottingham in the United Kingdom and specializes in Environmental Science, with a particular emphasis on ecology, global and planetary change, and environmental engineering. Their research spans various interconnected disciplines, including ecological modeling and media technology.

The scientist's work covers a range of topics primarily related to remote sensing applications. These topics include:

  • Remote Sensing in Agriculture
  • Species Distribution and Climate Change
  • Remote-Sensing Image Classification
  • Remote Sensing and LiDAR Applications
  • Land Use and Ecosystem Services
  • Advanced Image Fusion Techniques
  • Flood Risk Assessment and Management

Giles M. Foody has contributed significantly to the academic literature. Recent papers include:

  • "Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification," 2020, Remote Sensing of Environment
  • "Applications in Remote Sensing to Forest Ecology and Management," 2020, One Earth
  • "Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network," 2020, IEEE Transactions on Geoscience and Remote Sensing
  • "Active restoration accelerates the carbon recovery of human-modified tropical forests," 2020, Science
  • "Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient," 2023, PLoS ONE

Their frequent coauthors include:

  • Doreen S. Boyd
  • Yun Du
  • Feng Ling
  • Duccio Rocchini
  • Petra Šímová

The most common venues for their publications comprise:

  • Remote Sensing
  • Remote Sensing of Environment
  • IEEE Transactions on Geoscience and Remote Sensing
  • Zenodo (CERN European Organization for Nuclear Research)
  • Journal of Hydrology

Giles M. Foody was recognized as an IEEE Fellow in 2013 for contributions to the remote sensing of land cover, reflecting a notable aspect of their professional career.

Best Publications

  • Status of land cover classification accuracy assessment

    Giles M. Foody

  • Good practices for estimating area and assessing accuracy of land change

    Pontus Olofsson;Giles M. Foody;Martin Herold;Stephen V. Stehman

  • Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy

    Giles M. Foody

  • A relative evaluation of multiclass image classification by support vector machines

    G.M. Foody;A. Mathur

  • Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation

    Pontus Olofsson;Giles M. Foody;Stephen V. Stehman;Curtis E. Woodcock

  • Feature Selection for Classification of Hyperspectral Data by SVM

    Mahesh Pal;Giles M Foody

  • Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions

    Giles M. Foody;Doreen S. Boyd;Mark E.J. Cutler

  • Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification

    Giles M. Foody;Ajay Mathur

  • Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

    Giles M. Foody

  • Key issues in rigorous accuracy assessment of land cover products

    Stephen V. Stehman;Giles M. Foody

  • Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification

    Giles M. Foody

  • The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM

    Giles M. Foody;Ajay Mathur

  • Assessing the Accuracy of Remotely Sensed Data: Principles and Practices

    G. Foody

  • Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification

    G. M. Foody;N. A. Campbell;N. M. Trodd;T. F. Wood

  • Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions

    G. M. Foody;D. P. Cox

  • Measuring and modelling biodiversity from space

    Thomas W. Gillespie;Giles M. Foody;Duccio Rocchini;Ana Paula Giorgi

  • On the compensation for chance agreement in image classification accuracy assessment

    G. M. Foody

  • Multiclass and Binary SVM Classification: Implications for Training and Classification Users

    A. Mathur;G.M. Foody

  • Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI–rainfall relationship

    G.M Foody

  • Harshness in image classification accuracy assessment

    Giles M. Foody

  • An evaluation of some factors affecting the accuracy of classification by an artificial neural network

    G. M. Foody;M. K. Arora

Frequent Co-Authors

Doreen S. Boyd
Doreen S. Boyd University of Nottingham
Paul J. Curran
Paul J. Curran City, University of London
Peter M. Atkinson
Peter M. Atkinson Lancaster University
Linda See
Linda See International Institute for Applied Systems Analysis
Steffen Fritz
Steffen Fritz International Institute for Applied Systems Analysis
Duccio Rocchini
Duccio Rocchini University of Bologna
Richard Lucas
Richard Lucas Aberystwyth University
Carlo Ricotta
Carlo Ricotta Sapienza University of Rome
Harini Nagendra
Harini Nagendra Azim Premji University
Jadunandan Dash
Jadunandan Dash University of Southampton

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