# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
###### Abstract  
The extraction of a road network is critical for city planning and has been widely studied in previous research using high resolution images, whereas the high cost of high-resolution remote sensing data and the complexity of its analysis also cause huge challenges for the extraction. The successful launch of a high resolution (130 m) nighttime remote sensing satellite, Luojia 1-01, provides great potential in the study of urban issues. This study attempted to extract city roads using a Luojia 1-01 nighttime lighting image. The urban regions were firstly distinguished through a threshold method. Then, an unsupervised PCNN (pulse coupled neural network) was established to extract the road networks in urban regions. A series of optimizing methods was proposed to enhance the image contrast and eliminate the residential regions along the roads. The final extraction results after optimizing were compared with OSM (OpenStreetMap) data, showing the high precision of the proposed approach with the accuracy rate reaching 83.2%. We also found the precision of city centers to be lower than suburban regions due to the influence of intensive human activities. Our study confirms the potential of Luojia 1-01 data in the extraction of city roads and provides new thought for more complex and microscopic study of city issues. Luojia 1-01; nighttime light imagery; road extraction; urban regions +
Footnote †: journal:visual observation is very high. The combination of a priori knowledge obtained from human observation and powerful computation ability from the computer system have improved the extraction accuracy. The main steps of the semiautomatic methods are as follows: (1) Artificially choose several initial road seed points in original images and record the road directions. (2) Put the initial road seed points into a computer system and formulate the specific growth pattern to distinguish the pixels with a high probability of being road. (3) Make an optimization of the initial extraction results in aspects such as shape, topotaxy and other a priori knowledge. Many studies have proposed or improved the semiautomatic extraction models, such as the snake model [12], dynamic programming algorithm [2], region growing method [13] and edge tracking method [14]. The limitation of the semiautomatic methods is also obvious: it is over-reliant on prior knowledge, which means the final extraction accuracy is largely determined by the quality of the prior knowledge, whereas the key process during the acquisition of prior knowledge--road networks' recognition and seed points' selection--needs a mass of time-consuming and high-cost artificial work. In addition, the characteristics of road networks in different places are often different, which means the prior knowledge obtained through the labor-intensive preparation work can only be used in specific areas. Therefore, some studies have attempted to establish the automatic extraction models, which do not need prior knowledge [5; 15]. The automatic methods can distinguish road regions and non-road regions automatically. The extraction process is conducted according to the essential characteristics and judging criteria of the road network, through which the feed points are distinguished and then extended to the road sections. The road sections are finally connected with each other and formed into road networks through a series of optimizing processes. Although there exist several typical extraction methods, including the parallel lines method, spectral classification method and mathematical morphology, the accuracy of these methods is generally low compared with semiautomatic methods. The current characteristics are still limited for a computer system to extract road networks through remote sensing images. A considerable number of studies have realized the automatic methods can be the future development tendency and then attempt to introduce new methods into the road extraction studies, including deep learning technology.