## 1 Introduction  
Urbanization, with its rapid and continuous expansion, has brought significant transformations to cities, but it has also generated increasingly complex environmental [1], social, and economic [2] challenges. Issues such as light pollution [3, 4], high energy consumption [5], and natural hazards such as hydrogeological risks emphasize the urgency of adopting innovative solutions based on accurate data and multidimensional analysis. In detail, [6] provides a review of the scientific literature on light pollution, while [7] proposed a multimodal machine learning model called LPS-Net (Light Pollution Assessment Network Model) to assess light pollution by considering various indicators such as ambient lighting, light color, and exposure time to the light source. Regarding the hydrogeological hazard, [8] proposed a review of flood modeling in urban developments, highlighting the various strengths and weaknesses and emphasizing the importance of model calibration to adapt them to local situations and case studies. The paper also provides valuable insights into understanding the evolution of flood hazards at different scales. Emerging technologies, such as 3D platforms and digital twins, have proven to be extremely effective tools for representing, analyzing, and simulating complex scenarios [9; 10; 11], offering a visually intuitive and technically advanced approach to addressing challenges related to sustainability and urban resilience. For example, researchers in [12] proposed a 3D platform able to visualize energy data of buildings; another study [13] proposed a 3D platform, enhanced with AI, to provide improved damage evaluation and modeling. In the latter, Ref. [14] explores the role of digital platforms in providing faster responses and improving urban resilience. In this context, the urban digital twin enables the integration of geomatic [15; 16; 17], infrastructural, and meteorological data into a single platform, supporting strategic decision-making processes. The ability to create realistic simulations and predictive analyses promotes the adoption of more informed and efficient land management policies. The concept of a digital twin is relatively new and still in its early stages, but extensive research and practical applications are gaining momentum in all fields of research, from the creation of digital twins for industries and plants, to cars and airplanes, to road structures and infrastructure, and even to healthcare facilities (hospitals and outpatient clinics). In this context, this research work aims to demonstrate the applicability and potential of the urban digital twin in sustainable territory management and environmental risk assessment, overcoming some of the characteristic limitations of its implementation. Specifically, in the following:  
* Public lighting management, aimed at reducing light pollution and improving energy efficiency. * Hydrogeological risk assessment, with simulations of flood events to identify vulnerable areas. To achieve this goal, various data sources were used to characterize the studied phenomena (lighting and hydrogeological risk), acquired through different Geomatics and Earth Observation techniques, including satellite data, drone data, GNSS measurements, and ground-truth sensors. For the visualization and management of the collected data, a system based on Cesium technology was implemented, enabling the operation of three-dimensional and geospatial data within a single, interactive environment. The development of this integrated and updatable system provided the current state of the chosen study area, a portion of the metropolitan city of Reggio Calabria. The digital twin was developed in an open-source environment with open data, allowing users to upload their own data to the platform and use it with custom-developed models.