# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 5 Discussion  
Benefiting from the development of remote sensing technology, nighttime light data, as one of the new typical remote sensing data, has been widely used in the study of social economy. Additionally, the main nighttime light data sources used in previous studies were DMSP-OLS and NPP-VIIRS, with low resolutions of 1 km and 700 m, respectively. Therefore, they can only be used in the study of some macro problems. Although several high-resolution nighttime data, such as JL1-3B [49], have been introduced in previous studies, these data were not available to the public freely. Therefore, the study and application of high-resolution nighttime data is really lacking. Luojia 1-01, with its high resolution (130 m) and availability to the public, will potentially be used in the study of more microcosmic city issues, such as urban traffic evaluation. This study investigated the potentiality of using Luojia 1-01 data in the study of urban traffic. During the COVID-19 pandemic, major roads were found to be less used through NPP-VIIRS nighttime light data [50]. The Luojia-01 data set and the approach we proposed in this study may make  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region** & **Precision** & **Recall** & **F1** \\ \hline Case A & 0.913 & 0.907 & 0.910 \\ Case B & 0.826 & 0.880 & 0.852 \\ Case C & 0.785 & 0.849 & 0.816 \\ Case D & 0.826 & 0.880 & 0.852 \\ Whole City & 0.847 & 0.818 & 0.832 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy assessment of extraction results.  
it possible to further investigate the dynamic road occupation and traveling preferences during the pandemic.  
However, some challenges still exist in the extraction of road networks, which need to be further addressed. The first challenge is to distinguish the light intensity of residential buildings from urban roads. Although most buildings can be removed through their different shape with roads, some buildings nearby the roads may still affect the extraction results. The second challenge is the data lacking in some pixels, due to data noises, trees and the shielding of high buildings, which will cause some tiny cavities in the extraction results. Although morphologic optimizing methods have been introduced to fill these cavities, the accurate identification of these cavities is still difficult. The adoption of external data sources can be a potential solution, which needs further attempts. The third challenge is that the available Luojia 1-01 images are still limited. Therefore, it is still difficult to obtain the average light intensity data source with the more stable light intensity of urban roads, which will fix the flux reduction problem in public and private lighting at night.  
With the increase in the data size of the Luojia 1-01 satellite over time, the extraction precision of ground objects such as road networks and residential buildings will be highly improved. In future application, the combination of Luojia 1-01 data with high resolution images or Landsat images will also optimize the current road extraction project and reduce expenses significantly.