# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 4 Results
### Extraction Results of City Road  
The image after enhancement was then fed into the PCNN to extract the road networks in Wuhan. Four parts, namely A, B, C and D, were chosen as the case areas to show the extraction process. The locations of the four case areas are displayed in Figure 10.  
Figure 8: (**a**): the original urban regions; (**b**): the image after sharpening; (**c**): the image after sharpening and median filtering. (**d**): the case regions A and B.  
Case A is located in the north of Wuhan, where a wide range of forests exists. The coverage of the forests may shield the road lighting, thus leading to the discontinuity in the original image. Through the extraction process, the road network regions were  
Figure 10: (**a**): the locations of A, B, C and D in original nighttime light data; (**b**): the extraction results and comparison with OSM data.  
Figure 9: (**a**): the statistical information of pixels in original image; (**b**): the statistical information of pixels in sharpened image; (**c**): the spatial distribution of DN values in original image; (**d**): the spatial distribution of DN values in sharpened image.  
extracted with some residential areas, which were then eliminated through the optimizing process. Additionally, the rough outline was finally formed through the thin process. The comparison with the OSM network showed that the shape and direction of the main streets have been correctly extracted, except three short roads were missed.  
Case B is also located in the north of Wuhan. Additionally, the region mainly includes several riverside roads and a bridge called 'Yangtze River Bridge'. We can find the riverside road and the bridge has been extracted correctly. However, some short roads near the riverside road were just extracted partly caused by the mixed-up pixels in the connection of the riverside road and bridge, which also caused the small break between them.  
Case C is located in the middle, the city center, of Wuhan, covering several blocks, containing many high DN value pixels and two bridges. Due to the similarity of the DN values between the residential areas and road networks in city center, the identification of the road network is much more difficult than regions in suburban areas. Therefore, many crush plaques of residential areas were also extracted after the PCNN process. Through shape optimizing, most crush plaques have been effectively eliminated. The final extraction results were close to the OSM data, although a few short roads in the top left were not distinguished clearly.  
Case D is located in the south of Wuhan, covering the connection regions of two lakes. Through PCNN, the main road network regions have been extracted. A small break emerged in the extraction results, which was caused by the large difference of DN values along the roads.  
Table 3 shows the accuracy assessment of the extraction results. The F1 index of the four regions were 0.910, 0.852, 0.816 and 0.852, respectively. Additionally, the F1 index of the whole of Wuhan is 0.832. Overall, caused by the instability of the DN values and shield, a few short roads in some suburban regions may have been ignored or extracted out of shape, which caused the recall value of Wuhan to be lower than the precision value. Additionally, the precision of the extraction results in city centers with dense residential areas was lower than other regions, such as Case C, the F1 (0.849) of which is lower than the other three case regions. However, all the main roads have been correctly extracted through Luojia 1-01 nighttime lighting data. The extraction results were close to the OSM data.