## 4 Discussion  
The urban digital twin we proposed stands out in several ways. First, our approach focuses on two specific phenomena, aiming to study and analyze their characteristics and evolution over time: light pollution and hydrogeological risk. The key difference in our approach is that users will not need to download any program or install anything on their PC. With the data available, they will be able to work in the cloud on solutions that go beyond viewing the uploaded data, enabling the creation of complex pipelines in which the results of one step's processing become the inputs for subsequent steps. The aim is also to connect the incoming data so that inputs can modify the proposed urban digital twin through relationships designed and studied specifically for this purpose. In fact, models characterizing the two phenomena will be implemented so that the digital twin is not just a data viewer but an integrated system capable of modifying the graphical representation based on input variables. This is made possible thanks to the integration of an IoT system that provides real-time data. To evaluate a digital twin, several aspects must be taken into account, both from the point of view of the obtainable results and from that of the system performance. From the point of view of the expected results, the predictions of the digital twin can be compared with the real data coming from the physical system that it replicates, calculating common metrics such as the RMSE or determining the accuracy of the predictions, that is, the system's ability to correctly predict future events. From the point of view of performance and scalability, there are several methods to evaluate the performance of the digital twin, which mainly focus on the efficiency in simulating the physical system, on its scalability in managing large amounts of data, and on the reliability in processing the data. In this sense, the proposed system works in the cloud, significantly reducing the computational load compared to local solutions. Furthermore, the system is easily integrated and interoperable with other data, allowing it to interact correctly with other systems and technologies. In the future, evaluation metrics may also include user experience and performance monitoring over time, to perform periodic updates and optimize the parameters of the digital twin based on new information and evolutions of the physical system under study. The proposed urban digital twin mainly focuses on specific aspects related to light pollution and hydrogeological risk, but many other applications can be implemented in relation to smart cities. In fact, research is currently focused on identifying functional relationships and models to represent these complex phenomena, which, in themselves, not only require in-depth knowledge of the issues but also optimal and high-performance management of the numerous variables involved. Despite this, the urban digital twin could certainly include other aspects in the future, such as traffic management, air quality, waste management, and urban safety. As structured, the urban digital twin, with its open-source configuration, could certainly support additional integration across various aspects related to smart cities, helping public administration in different areas of efficient city management. It can also integrate data from various sources (in addition to IoT), including external platforms. Despite the strengths of the proposed model, some of the main limitations are related to the spatial resolution of VIIRS, which can lead to an underestimation of local illumination in dense urban areas and an overestimation in less illuminated areas. Furthermore, although VIIRS provides data with improved spatial resolution compared to previous satellites, the resolution remains lower than that of other high-resolution sensors. Moreover, VIIRS provides data daily, but daily coverage is not always complete due to atmospheric factors or satellite acquisition times.