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In rapidly expanding cities, an accurate land-cover information system can assist in making decisions for sustainable urban planning and governance. Point cloud procured using Airborne LiDAR Scanning (ALS) can be harnessed by creating an adaptable urban semantic information layout with querying capability. We propose a novel methodology that isolates spatial objects and integrates them with attribute information to enable object-based spatio-semantic queries. Experiments were carried out to demonstrate proposed methodology based on queries of different complexities. Results indicate that proposed approach is scalable, flexible, and hence opens a wide application perspective in urban planning, and sustainable smart city development. [ABSTRACT FROM AUTHOR]