## 5 Discussion  
We present an approach to reconstructing horizontal illuminances for three types of road lighting configurations, single-type, double-type, and staggered-type high-pole (\(\geq\)6 m) road lighting. These types of lighting cover most present road lighting. The basic assumption of applying the sine model and linear model is that local maximum illuminance may appear beneath or around the luminaire. There are three reasons that may explain why local maximum illuminance is likely to appear beneath the luminaire. First, most luminaires' intensities are relatively strongly downward, except the bat-wing type photometric curves. Second, the relation between illuminance and intensity conforms to the inverse square law, and the place beneath the luminaire is of the smallest distance from the luminaire. Third, two neighbor luminaires in road lighting are about 20-30 m away, and influence from neighboring luminaire is often insufficient to cause the illuminance between two luminaires to exceed the maximum illuminance beneath a single luminaire. Most road lighting may achieve these three conditions, and the proposed models may work well in most situations. The bat-wing photometric curve luminaires are designed for high uniformity, and illuminances at different measured points are close. Conforming to the proposed model or not, few errors may take place for bat-wing-type luminaires. Real road lighting measurements agreed well with the reconstructed illuminance distributions produced by our model, suggesting the model may be useful in providing reasonably accurate estimates of illuminance values for most road lighting situations. Although we did not apply the model to a road lighting setup in which lighting with a long arm that places the lamp over the center of the carriageway is installed, our model may work equally well with such lighting. In the transverse direction, the long arm may lead to the maximum illuminance appear in the middle lane, and minimum illuminances may appear at two sides, which may not influence the sine distribution in the longitudinal direction. In the transverse direction, as a linear function is applied for two neighbored points, and the linear model may still suit for such cases. There might be some special  
Figure 15: Lighting performance evaluation. (**a**) Mesopic luminance calculation based on reconstructed illuminance; (**b**) equivalent melanopic lux calculation based on reconstructed illuminance. ML represents mesopic luminance, and ME represents equivalent melanopic lux. cases for road lighting that may not work, such as luminaires with a low pole height, or neighboring luminaires that are relatively close together. Lighting has both positive and negative impacts on human beings, the environment, and ecology. Obtaining and using big lighting data can add value to the scientific investigation of these impacts. Remote sensing technology is a promising approach to obtaining big lighting data, but there are some limitations in resolutions, wavelength ranges [38], and intensity transformations at present. These cannot be solved in a short time. As mentioned above, two works have tried to obtain big lighting data. In [PERSON]'s work [39], although they succeed in detecting light source categories with photographs taken from a 900 m height, their illuminance transformations from image pixel intensities reached a 3-10-fold error at different illuminance levels. [PERSON]'s work [34] made assumptions about hemispherical isotropic point sources emitting at equal intensity in all directions, but this is not true for most luminaires and will lead to large errors. Furthermore, many details about the setup of luminaires are required. Their model is therefore of limited use. Our proposed reconstruction approach is dependent on measured feature points, guaranteeing an error within 20%. It is much more precise than reported approaches to obtain lighting data.