Abstract
Flooding is one of the most pervasive and destructive natural hazards, with its frequency and intensity expected to worsen under climate change. While advances in geospatial analytics, Internet of Things infrastructures, and artificial intelligence have enhanced urban data ecosystems, existing smart city platforms remain limited in their capacity to provide automated, real-time flood intelligence. Most platforms focus on delineating flood extent without extending to impact assessment, leaving critical gaps in disaster response and recovery. As a result, exposure analyses of residents, buildings, and infrastructure are often conducted manually, delaying emergency services, rescue operations, and longer-term recovery planning. This study introduces an Artificial Intelligence of Things-enabled urban platform designed for flood detection, impact mapping, and spatial decision support. The conceptual architecture integrates distributed sensing, satellite imagery, and pretrained deep learning models within the Esri ArcGIS ecosystem, operationalising the principles of platform urbanism for disaster management. Demonstration of the platform draws on the March 2022 flood event in South East Queensland, with a focus on selected suburbs in Logan and the Gold Coast. Using the Prithvi-Flood Segmentation model and harmonised Sentinel-2 imagery, the workflow automates the delineation of flood extent and links outputs with exposure analytics to identify affected suburbs, railway stations, roads, buildings, and residents. Future research should focus on fine-tuning pretrained models for local contexts and scaling the architecture to incorporate additional AI-driven modules, such as road extraction, infrastructure vulnerability assessment, or population displacement modelling, thereby extending the platform’s utility across multiple hazards and governance contexts.