AI-IoT for tropical heritage conservation: scoping review and case framework Lawang Sewu

Tropical heritage buildings face escalating threats from environmental stressors such as humidity fluctuations, temperature extremes, and urban air pollution challenges intensified by climate change. In Southeast Asia, these challenges are compounded by a pronounced technological gap in heritage conservation, highlighting climate justice concerns compared to Europe and East Asia. This paper presents a scoping review that follows systematic review protocols to ensure transparency and reproducibility, synthesising global and regional evidence on integrating artificial intelligence (AI) and the Internet of Things (IoT) into proactive, adaptive conservation frameworks for tropical heritage. Focusing on the Lawang Sewu building in Semarang, Indonesia, the study employs bibliometric analysis, environmental parameter mapping, and thematic synthesis to situate local conservation challenges within global trends. Findings indicate that AI-IoT convergence improves operational decision-making, enables early detection of structural and environmental risks, and optimises resource use in resource-limited tropical settings. The paper proposes a seven-phase conceptual framework integrating AI, IoT, heritage building information modelling, and Digital Twin technologies. Validated through preliminary field applications, this scalable framework advances equitable access to innovation and enhances resilience against climate-related impacts in tropical heritage conservation.