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April 11, 2026Computational Urban ScienceOpen Access

A decision support toolkit for deep learning application in Southern vitality research

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Authors

AAAlyaa AmerNANancy Abdel-MoneimHKHeba Allah Essam E. Khalil

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Overview

Introduces a toolkit to enhance urban vitality analysis in the Global South, suggesting a practical approach for using deep learning techniques.

Key Points

  • The aim is to create a decision-support toolkit for integrating deep learning into urban vitality research in the Global South.
  • Developed a toolkit featuring structured decision trees for urban vitality variables, data sources, and deep learning tasks.
  • Created a Weighted Sum Model to rank deep learning algorithms based on literature relevance.
  • Implemented a labelling system to address data accessibility and potential biases in Global South contexts.
  • Validated the toolkit through various grounded case studies from existing literature.
  • The toolkit facilitates informed methodological choices for researchers in the Global South.
  • It highlights the challenges and considerations unique to Southern urban contexts.
  • The decision-support approach lowers barriers to entry for deep learning applications in underrepresented areas.

Cite This Study

Amer et al. (2026) studied this question.

synapsesocial.com/papers/69d9e6b078050d08c1b76fd7https://doi.org/10.1007/s43762-026-00251-y
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