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Technologically assisted decision-making in urban planning and governance is significant to envisage Sustainable Development Goal (SDG) 11, of developing sustainable cities and communities. In the current millennium, planners and decision-makers require knowledge-rich virtual models for managing the man-made and natural resources in the city. To effectively utilize the technological advancement for sustainable urban development, there is a need for expeditious entail towards accurate urban resource mapping, development of flexible monitoring and information extraction framework, and enticing visualization. Airborne LiDAR Scanning (ALS) is capable of producing very precise 3D geometric data over expansive urban areas in a timely and economically efficient manner. However, it is challenging to derive viable outcomes from the unstructured and voluminous point cloud. This work proposes an intermediary metamorphosed point cloud storage framework to enhance the utility of point clouds for multiple pragmatic applications. The proposed methodological approach transforms the unstructured, massive point cloud into an ontologically stored urban object collection to utilize the large-scale urban point cloud in decision-making. Further, the paper demonstrates the direct applicability of the metamorphosed point cloud storage framework for two specific applications related to sustainable urban development. Experiments carried out using the proposed framework on DALES benchmark dataset show promising results. [ABSTRACT FROM AUTHOR]