To realize the United Nations Sustainable Development Goals (SDGs), especially those focused on promoting sustainable urban development and improving community well-being, the integration of digital technologies has become essential for modern cities. These technological innovations play a vital role in the planning, implementation, and administration of urban spaces, ensuring alignment with tenets of sustainability and resilience. Recent advancements in sensor technology and data processing methodologies have enabled precise modelling of the physical urban environment, which is essential for sustainable urban development initiatives. In urban areas with dense vegetation and vertical urbanization, it is essential to capture and analyse the urban fabric in three dimensions. Advanced remote sensing technologies like Airborne Laser Scanning (ALS) can generate detailed three-dimensional representations of landscapes through point cloud datasets. However, current urban representation often overlooks vegetation, focusing on buildings, limiting overall comprehensiveness. This chapter seeks to establish a framework and methodology for a comprehensive near-realistic representation of urban areas to enhance governance and support sustainable development. The study focuses on developing methods for efficiently representing complex urban objects, extracting geometric properties from diverse elements, and implementing a flexible schema to ensure consistency and compatibility for application across multiple domains. As a case study, the proposed framework is implemented on ALS data over Thiruvananthapuram, Kerala, India, a city characterized by a blend of diverse vegetation and urban development. The chapter also demonstrates how the proposed framework can contribute to achieving the three pillars of Sustainable Development Goal 11: mapping and monitoring, community involvement, and data-driven decision-making.