[Geo4All] IJGIS Special Issue on Deep Learning Approaches in Geographical Information Science and Human Geography

[De Sabbata, Stefano (Dr.)]

Dear all,

The official call for papers for our new Special Issue of the International Journal of Geographical Information Science on Deep Learning Approaches in Geographical Information Science and Human Geography is now live.

https://think.taylorandfrancis.com/special_issues/deep-learning-approaches-geographical/

Deep neural networks have had a transformative impact across a wide range of fields, gaining significant traction among researchers in academia and industry. Traditional methods in artificial intelligence and machine learning have long been part of Geographical Information Science (GIScience) and geocomputation, including research both on unsupervised learning approaches to geographic data mining (e.g., geodemographic classification and dimensionality reduction) and supervised methods of inference (e.g., spatial autocorrelation and geographically weighted regression). However, while deep machine learning has found wide use in remote sensing and earth observation, its application to human geography has been neglected until recently (Harris et al., 2017). Research works highlight the great potential of deep learning to study geographic phenomena: Xu et al. (2017) proposed the use of deep autoencoders to perform quality assessment of building footprints for OpenStreetMap; De Sabbata and Liu (2019) explored a geodemographic classification approach based on deep embedding clustering; Palmer et al. (2021) have been exploring the use of street-view data in public health studies.

This special issue develops from discussions that emerged at the Deep learning approaches in GIScience session of the Annual International Conference of the Royal Geographical Society (with IBG), but submissions are open to all interested authors. In particular, we welcome submissions focused on novel spatially-aware deep learning approaches and applying recent approaches to human geography topics in a novel way. Application areas include human geography, demography, digital geographies, public health, social equity and justice, sustainability and resilience, transport science and urban planning, and the digital humanities.

Relevant topics include, but are not limited to:

  *   Geographic theory in deep learning approaches
  *   Spatially-aware deep learning approaches
  *   Deep learning approaches to analyse geospatial vector data
  *   Deep learning approaches using quantitative-qualitative mixed-method
  *   Deep learning approaches to geographic information retrieval and natural language processing
  *   Deep learning approaches in geovisualisation
  *   Deep learning applications with unstructured data or new data sources, including using data from street-view, drone or small low-cost satellites
  *   Critical analysis of geographic deep learning
  *   Novel geospatial datasets for geographic deep learning
  *   Open research problems in applying deep learning methods in GIScience

Important Dates

  *   Abstracts (no more than 250 words): March 1st, 2022
  *   Decisions on abstracts: March 15th, 2022
  *   Full manuscripts: August 15th, 2022
  *   Initial editorial decisions: late 2022
  *   Accepted manuscripts online:  1-2 weeks after final acceptance on each manuscript
  *   Anticipated publication of the special issue: late 2023

Special Issue Guest Editors

  *   Stefano De Sabbata, University of Leicester (s.desabbata at le.ac.uk<mailto:s.desabbata at le.ac.uk>)
  *   Andrea Ballatore, King’s College London (andrea.ballatore at kcl.ac.uk<mailto:andrea.ballatore at kcl.ac.uk>)
  *   Godwin Yeboah, University of Warwick (g.yeboah at warwick.ac.uk<mailto:g.yeboah at warwick.ac.uk>)
  *   Harvey Miller, Ohio State University (miller.81 at osu.edu<mailto:miller.81 at osu.edu>)
  *   Renee Sieber, McGill University (renee.sieber at mcgill.ca<mailto:renee.sieber at mcgill.ca>)
  *   Ivan Tyukin, University of Leicester (i.tyukin at leicester.ac.uk<mailto:i.tyukin at leicester.ac.uk>)

If you have any query, please do not hesitate to contact me.

All the best,
Stef.


“It’s all going to be alright in the end, and if it's not alright, it's not the end”
(by Ol Parker, via Wittertainment)

Dr Stefano De Sabbata (they/them)
Lecturer in Quantitative Geography
School of Geography, Geology and the Environment,
University of Leicester, University Road, Leicester, LE1 7RH, UK
Research Associate at the Oxford Internet Institute, University of Oxford
t: +44 (0)116 252 3812
e: s.desabbata at le.ac.uk<mailto:s.desabbata at le.ac.uk>
w: sdesabbata.github.io<https://sdesabbata.github.io/>
w: le.ac.uk/departments/geography/people/stefano-de-sabbata<http://www2.le.ac.uk/departments/geography/people/stefano-de-sabbata>
w: oii.ox.ac.uk/people/stefano-de-sabbata<https://www.oii.ox.ac.uk/people/stefano-de-sabbata/>
twitter: @maps4thought<https://twitter.com/maps4thought>

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.osgeo.org/pipermail/geoforall/attachments/20220121/91916cc6/attachment.html>