Local governments face critical challenges in the era of digital transformation, balancing the responsibility of safeguarding resident information and administrative documents while maintaining data integrity and public trust. These responsibilities become even more critical as they transition into smart cities adopting advanced technological innovations to revolutionize governance, enhance service delivery, and foster sustainable and resilient urban environments. Technological advancements like Internet-of-Things devices and artificial intelligence-driven approaches can provide better services to residents, but they also expose local governments to cyberthreats. There has been, nonetheless, very little study on cybersecurity issues from the local government perspective, and information on the multifaceted nature of cybersecurity in local government settings is scattered and fragmented, highlighting the need for a conceptual understanding and adequate action. Against this backdrop, this study aims to identify key components of cybersecurity in a local governmental context through a systematic literature review. This review further extends to the development of a conceptual framework providing a comprehensive understanding of the local government’s cybersecurity landscape. This study makes a significant contribution to the academic and professional domains of cybersecurity issues and policies within the local governmental context, offering valuable insights to local decision-makers, practitioners, and academics. This study also helps identify vulnerabilities, enabling stakeholders to recognize shortcomings in their cybersecurity and implement effective countermeasures to safeguard confidential information and documents. Thus, the findings inform local government policy to become more cybersecurity-aware and prepared.

Cybersecurity is a crucial concern for local governments as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of a well-structured policy is equally important as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with the National Institute of Standards and Technology’s Cybersecurity Framework 2.0, which is a widely adopted and commonly used cybersecurity assessment framework. This review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. This study further extends the development of a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure.

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures.

Cybersecurity is a crucial concern for local governments, as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of well-structured policy is equally important, as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with NIST CSF, which is a widely adopted and commonly used cybersecurity assessment framework. The review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. The study further extends to developing a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure.

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.

The increasing availability of pretrained Geospatial Artificial Intelligence (GeoAI) models has accelerated the operational adoption of deep learning within geographic information systems. However, models developed in one geographic region frequently experience performance degradation when deployed elsewhere because of geographic domain shift. Although transfer learning is widely used to improve cross-regional model performance, it remains unclear whether improved predictive accuracy alone provides sufficient evidence for trustworthy deployment. This study investigates this question using rooftop solar photovoltaic mapping in Brisbane, Australia, as an operational case study. Esri’s pretrained US Solar Panel Detection model was locally calibrated through transfer learning and evaluated using both development and independent local test datasets within Brisbane. Local calibration substantially improved detection performance, increasing recall from 38.5% to 84.1% on the development dataset, with similar improvements observed on the independent 10-grid test dataset, indicating effective local adaptation within the sampled Brisbane environments. However, independent evaluation and qualitative error analysis revealed persistent false positives, missed detections, and geometric segmentation errors, highlighting residual uncertainty and the limitations of model performance within the evaluated geographic context. These findings suggest that transfer learning can substantially improve detection performance and provide useful evidence for assessing operational trustworthiness, although predictive improvements alone are insufficient to establish trustworthy deployment. By integrating complementary evidence relating to robustness, reliability, generalisation, transparency, and residual uncertainty, this study provides an evidence-based operational approach for assessing pretrained GeoAI models and informing responsible deployment decisions in geographically distinct environments.