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Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

This method uses a deep learning (DL) technique, using a DCNN to examine the physical characteristics of neighborhoods through medium-resolution (10 m) satellite imagery. Building on the foundational work of Maharana and Nsoesie [27] and Nguyen et al [28], who used DCNNs to analyze urban Google Street View (Google LLC) images focusing on predefined features, such as crosswalks, building types, and greenery, our study expands the scope to both urban and rural areas.

Butros M Dahu, Solaiman Khan, Imad Eddine Toubal, Mariam Alshehri, Carlos I Martinez-Villar, Olabode B Ogundele, Lincoln R Sheets, Grant J Scott

JMIR AI 2024;3:e64362

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