Predicting Adult Obesity Prevalence in Missouri Using Satellite Imagery and Deep Learning: A Multidisciplinary Approach to End-User Development and Prompt-Driven Innovation.

The integration of Generative AI and satellite technology offers groundbreaking potential for addressing chronic health issues like obesity in public health research. This chapter explores the innovative application of deep learning and satellite imagery to predict obesity prevalence in Missouri, emphasizing a multidisciplinary approach that integrates public health, artificial intelligence, and remote sensing. Utilizing medium-resolution Sentinel-2 satellite imagery, deep convolutional neural networks (DCNNs), specifically ResNet-50, are employed to extract deep neural visual features (DNVFs) from each census tract. This method transcends traditional data sources by integrating obesity prevalence estimates from the Centers for Disease Control and Prevention (CDC) with advanced image processing techniques to provide a nuanced analysis of environmental and societal determinants of health. By focusing on specific census tracts, the study illustrates how localized data can inform targeted interventions, thereby enhancing the effectiveness of health policies and practices. This approach not only reflects the practical application of user innovation in the field of public health but also sets a precedent for the future use of Generative AI to tackle complex health disparities. The findings encourage further exploration into the potential of this technology to expand its application to a broader geographical scale, thereby maximizing the impact of AI-driven public health initiatives.