The digitalization of the built environment for urban energy planning enables the integration of renewable energy systems, and informed decision-making. At urban scale, the acquisition of the point cloud is highly dependent on weather conditions and location of survey stations, as sunlight reflections on the lens can cause inconsistencies in the data acquisition. Therefore, this study investigates the variability of color information collected from nine 3D survey stations, validating results with punctual color measurements. The Leica ScanStationP30 was used to capture images and points of a portion of the NTNU Gløshaugen campus in Trondheim, Norway, and create a 3D-colored point cloud, associating an intensity value, geographic coordinates, and RGB data for each point. Based on these parameters, the building facades were extracted through a clustering process that distinguishes between buildings, trees, and the ground surface. Then, planar segmentation and clustering algorithms, based on spatial point density, effectively classified façade materials. A colorimeter was used to provide benchmark color data for each material and validate Leica ScanStationP30’s outputs. Statistical analyses were performed to evaluate the significance of weather conditions and station location in the color detection. The analysis highlighted a significant difference between the RGB values estimated by the Leica scanner and the reference values obtained from the colorimeter, emphasizing the needs for validation to improve data reliability. Assigning a consistent value for RGB to the points, based on the materials, enables various environmental analysis (e.g. solar analysis, urban heat island monitoring) using, for example, reliable solar reflections and albedo values.