LEVERAGING LARGE ARTIFICIAL INTELLIGENCE MODELS FOR THE REVITALIZATION OF SUAKIN ISLAND, SUDAN

Abstract

Suakin Island, located on the Red Sea coast of eastern Sudan, represents one of the most significant yet endangered examples of coral-stone Islamic architecture in the world. Once a thriving Ottoman-era port city, Suakin's distinctive urban fabric — characterized by mashrabiyas (wooden lattice screens), multi-story coral-stone mansions, arcaded markets, and intricately carved wooden doors — has suffered severe deterioration over the past century. This paper proposes and details a comprehensive framework for applying Large Artificial Intelligence (AI) Models to the digital revitalization, documentation, classification, and predictive restoration of Suakin's architectural heritage and that of broader eastern Sudan. Drawing on the latest advances in Large Language Models (LLMs), Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Generative Adversarial Networks (GANs), and diffusion-based generative models, we outline a full pipeline: from field data collection and feature engineering of architectural elements, to training supervised classifiers and generative restoration models. We benchmark applicable methods from analogous heritage contexts (Chinese, Iranian, Greek, and Indonesian vernacular architecture) where accuracies exceeding 90–98% have been achieved. We identify critical research gaps — the absence of any dedicated dataset or end-to-end AI model for Suakin — and propose concrete methodological pathways to address them. The study further introduces ArchGPT-style reasoning agents and multimodal CLIP-based retrieval as novel tools for supporting architectural conservation professionals in Sudan. Our findings establish that, given targeted data collection and culturally grounded model training, AI-based systems can serve as transformative instruments for the preservation and revitalization of Suakin Island's irreplaceable built heritage.