Submit a Manuscript to the Journal

Big Earth Data

For an Article Collection on

Earth Observation and GeoAI for Social Science

Manuscript deadline
01 April 2025

Cover image - Big Earth Data

Article collection guest advisor(s)

Dr Siqin (Sisi) Wang, SpatialSciences Institute, University of Southern California
[email protected]

Professor Chaowei Yang, Geography & Geoinformation Science Department, George Mason University
[email protected]

Dr Yongze Song, School of Design and the Built Environment, Curtin University, Perth, Australia
[email protected]

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Earth Observation and GeoAI for Social Science

GeoAI combines Geographic Information Systems (GIS) with Artificial Intelligence (AI), enabling social scientists to analyze spatial data with unprecedented accuracy and depth, while earth observation data have important strengths for social sciences as an important data source to quantify the change of urban space, human settlement, and economic development. GeoAI applications using big earth observation data in social science focus on examining the complex relationship between human behaviors and environment by using GeoAI algorithms to identify patterns and trends in large datasets that might be missed by traditional methods widely used in social science (e.g., surveying) as well as to predict social trends and phenomena based on historical data, aiding in proactive decision-making. GeoAI facilitates the study of multifaceted social issues that have spatial components, including in the domain of urban planning (the spatial dynamics of urban growth, transportation networks, and housing markets), public health (the spread of diseases, environmental health risks, and access to healthcare facilities), and environmental justice (the spatial distribution of environmental benefits and burdens among different social groups).

The importance of earth observations and GeoAI in social science lies in its ability to reveal the complex relationship between human behaviors and environment or even its causality and mechanism, enhance our understanding of spatial dimensions of social issues, improve policy-making, foster interdisciplinary collaboration, and drive technological advancements to be human-centered and resolve problems linking to population, community and society as a whole. The advances of GeoAI algorithms enable researchers to explore the confounders lying underneath the complexity of human-environment interaction. By leveraging the strengths of both GIS and AI, social scientists can address complex societal challenges more effectively and contribute to more informed and equitable decision-making processes.

This Article Collection invites original articles, review articles, data articles, and technical notes (all submissions are required to provide sharable datasets, codes, algorithms packages, workflow and software tools, publicly accessible by readers).

The main topic of “Earth Observation and GeoAI for Social Science” includes the two categories of subtopics as below:

Technical subtopics (earth observation data usage as the essential in the research design):

  1. GeoAI technical advances
  2. Generative GeoAI including ChatGPT, and other types of large language models
  3. GeoAI modelling and algorithms
  4. Machine learning, deep learning and neural network
  5. Natural language processing
  6. Computer vision
  7. Global and local models
  8. Remote sensing and GeoAI
  9. Earth observation data used in GeoAI

Disciplinary subtopics:

  1. Computation social science
  2. Human Geography
  3. Urban Geography

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor(s) for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is 1 April 2025.

Please contact Alex Johnson at [email protected] with any queries and discount codes regarding this Article Collection.

Please be sure to select the appropriate Article Collection from the drop-down menu in the submission system.

The Guest Advisors of this Article Collection have declared no conflict of interest in line with Taylor & Francis' editorial policies.

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All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Guest Advisors for this collection will not be involved in peer-reviewing manuscripts unless they are an existing member of the Editorial Board. Please review the journal Aims and Scope and author submission instructions prior to submitting a manuscript.