## 6 Conclusions  
In this work, we reconstructed urban road lighting illuminances with nine feature points between two luminaires. We proposed sine models for reconstruction in longitudinal directions and linear models in transverse directions based on analyses of 10 simulated road lighting setups and 8 real road lighting setups measured in England. We reconstructed illuminance for three simulated roads and two real roads and compared them with reconstructed illuminances based on the nine feature point models we developed. Mean differences between the simulated and reconstructed illuminances were 15.2% for single-type lighting, 10.5% for staggered-type lighting, and 19.0% for double-type lighting, and those between the real and reconstructed illuminances were 6.0% for single-type lighting, and 11.6% for double-type lighting. The overall average illuminance across the whole of each road estimated from the reconstructed illuminances differed from overall averages from the simulated illuminances by 8.9%, 5.0%, and 17.8%, and the real measurements by 2.1% and 6.9%. We believe these margins of error are acceptable in practical applications given the significant logistical savings offered by taking only nine measurements between luminaires, compared with current measurement standards. Although not clearly stated empirically in existing literature, a \(\pm\) 20% error in illuminance measurements may be acceptable. Existing road lighting guidance 1 specifies recommended average horizontal illuminances for different classifications of roads, such as S-class. The difference between adjacent category levels within a class of road does not exceed 20%, so even an error of \(\pm\)20% in terms of illuminance reconstruction would not result in a change in the categorization of the road. Based on present results and analysis, it is likely that, for most situations, the average error for reconstructed illuminance using our model may be within 20%. Further illuminance reconstruction work for three long roads shows that it is feasible to obtain more lighting data with a few measured feature points. Based on the reconstructed illuminance, we also demonstrate that mesopic luminance and equivalent melanopic illuminance can be easily calculated. The illuminance reconstruction work may also therefore help for evaluation of lighting efficiency and healthy performance in urban areas. The obtained illuminance data may also be used to study other impacts of lighting on areas such as the environment, local ecology, and crime, and the precision for the present reconstructed works are adequate. There are two other major advantages to our approach. First, the approach may work together with remote sensing technology, and requirements on imagery resolution and data storage can be decreased, as only features points are required. Second, the approach can augment manual measurement of an area of interest. In the future, it may not be possible to obtain real-time lighting data for some areas, even if the remote sensing technology is sufficiently advanced, and we are likely to still have to rely on manual measurements. However, as shown from the field measurements in Section 4, the lighting distributions along a single road may vary between significantly depending on the area of the road that is measured. This indicates the traditional way of measuring road lighting using one section of the road to represent its entirety may not be very precise. Our reconstruction method would allow a more accurate estimate to be made of the illuminance distribution along the full length of the road, without the need for additional measurement points. Conceptualization, Q.Y. ; Methodology, Q.Y. ; Software, Q.Y. ; Validation, Q.Y. and J.U. ; Formal Analysis, Q.Y. ; Investigation, H.W. and X.Z. ; Resources, Q.Y., H.W. and X.Z. ; Data Curation, Q.Y. ; Writing--Original Draft Preparation, Q.Y. ; Writing--Review & Editing, Q.Y. and J.U. ; Visualization, Q.Y. ; Supervision, Q.Y. ; Project Administration, Q.Y. ; Funding Acquisition, Q.Y.