(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
The convergence of hyperspectral imaging, Light Detection and Ranging (LiDAR), and conventional camera data presents unprecedented opportunities for smart city applications, including environmental monitoring, infrastructure assessment, and urban planning. However, integrating these heterogeneous modalities remains challenging due to disparities in spatial resolution, spectral dimensionality, and geometric representation. This paper proposes a hierarchical multi-scale attention network architecture that systematically fuses hyperspectral, LiDAR, and camera data at multiple abstraction levels. The framework employs dedicated encoders for each modality, followed by cross-attention modules that align features across scales and sensor domains. A hierarchical aggregation mechanism then integrates local, regional, and global contextual cues, enabling robust urban feature extraction. Beyond technical design, the paper critically examines structural trade-offs between computational efficiency and model capacity, between centralised cloud processing and distributed edge deployment, and between interpretability and predictive accuracy. Governance and policy considerations are addressed, including data ownership, privacy preservation, and equitable sensor coverage across socioeconomically diverse urban zones. Sustainability aspects such as energy consumption during inference and sensor life-cycle management are analysed. The proposed architecture is positioned within the broader landscape of large-scale socio-technical systems, where robustness, fairness, and long-term maintainability are as important as algorithmic performance. Case illustrations from recent fusion benchmarks and real-world pilot deployments underscore the practical challenges and opportunities. By foregrounding system-level reasoning, this paper provides a comprehensive framework for designing trustworthy and scalable multi-modal sensing infrastructures in smart cities.