## 2 Materials and Methods
### Implementation of the Proposed Methodology  
The first phase involved the collection of heterogeneous data from different data sources. In particular, we gathered static data for mapping the territory (data from drones and satellites) and dynamic data for real-time monitoring (data from sensors and meteorological stations). A technique for mapping the territory on a large scale is certainly remote sensing, which includes both satellite images and drone images. For this research work, different types of satellite images were acquired from various services available free of charge. In relation to the phenomenon of light pollution, data from the VIIRS (Visible Infrared Imaging Radiometer Suite) sensor was collected, visualized, and analyzed [42]. The sensor is equipped with 22 spectral bands, ranging from visible wavelengths (0.4-0.9 \(\upmu\)m) to thermal infrared (10.3-12.5 \(\upmu\)m), covering the visible spectrum, SWIR, TIR, and microwave. Mounted on satellites in polar orbit, the sensor provides detailed measurements of artificial lights during the night and offers sufficient resolution (up to about 750 m per pixel) to detect and monitor variations in light pollution in relation to the thermal bands, while the resolution is 1.5 km per pixel. The data are distributed by portals such as NOAA CLASS, NASA Earthdata, and VIIRS Nighttime Lights and provide global daily coverage thanks to the use of the Day/Night Band (DNB) channel, which is particularly sensitive to light even in low-light conditions, such as during the night phases. Several images were then acquired from this dataset over the study area to analyze changes in artificial brightness (expressed in radiative flux) and identify the most sensitive areas in relation to this portion of the territory. Alongside this dataset, data from the Copernicus Earth Observation service were also acquired, offering a wide range of satellite images and atmospheric variables. Specifically, data on carbon dioxide was obtained through the CAMS service (Copernicus Atmosphere Monitoring Service), including the CAMS global emission inventories [43]. This service also provides accurate estimates for other greenhouse gases, such as methane, nitrogen oxide, and other polluting compounds. It also offers both concentration distribution estimates and flux data (emissions and natural absorption). Regarding dynamic data, sensor data were used. First, an accurate and detailed mapping of the light sources was carried out by conducting a census with GNSS Instruments (Rover GNSS Sanding T5). For each light point, the precise latitude and longitude coordinates were recorded, ensuring accurate georeferencing. Each light source was cataloged based on the type of lamp used (e.g., LED or traditional), the power expressed in watts, the color temperature (Kelvin), and the maintenance status to identify any critical issues. The aim was to conduct an in-depth comparison of the performance of LED lamps, recognized for their high efficiency and sustainability, while identifying possible areas for system optimization and intervention. Regarding the phenomenon of hydrogeological risk, the data were acquired from meteorological stations situated in various areas. The annual datasets provide information on precipitation and flow data from the stations distributed across the metropolitan city, which were processed to make them compatible with the proposed system. Additionally, data in GeoJSON format of buildings and watercourses were retrieved. After the first phase, which focused on the collection of data essential for characterizing the phenomena of light pollution and hydrogeological risk, we proceeded to the second phase, which involved the processing and integration of this data into a structured database. This database can be visualized within a 3D geospatial data visualization system. For the management and visualization of geospatial data, it was decided to use the Cesium library, which was conveniently integrated into an HTML file running on a local server.