# Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
## 2 Materials and Methods
### Methodology
#### 2.2.1 Interactive Single-Road Extraction
However, it was easily influenced by the effect of 'different objects with similar spectra'. Objects such as concrete ground and bare soil were extracted as roads, leading to low precision, accuracy, and IoU (Table 3). Because of the limited working area, the developed method could avoid over-extraction (Figure 17b,e) and thus reach better precision, accuracy, and IoU (Table 3). When shadows occlude some roads, such as those in Sample 3, [PERSON]'s method could hardly identify these roads (Figure 16g), significantly reducing its recall and IoU (Table 3). In the developed method, roads were thinned to centerlines and regenerated with a certain width, which weakened the influence of shadows (Figure 17h) and achieved an acceptable result (Table 3). Furthermore, the series of operations including morphological operation and vector simplification in the developed method eliminated the burs and made the road



## 4 Discussion

After applying the method in four different tested images, the results all have high accuracy (Table 1). For simple roads in rural areas, such as Data 1, the proposed method has high precision, accuracy, recall, and IoU (Table 1). For suburban roads such as Data 2, the proposed method can accurately extract the main roads and obtain high precision and accuracy (Table 1). For urban areas such as Data 3 and rural residential areas in Data 4, the method is robust to shadows and still achieves good extraction results in areas with buildings (Table 1, Figure 16). In terms of extraction efficiency, the longer the road, the shorter the road extraction time per 1000 pixels (Table 2). This is mainly because most of the time consumption in this method focuses on image processing at the grid level, while the algorithm at the vector level is very fast. The longer the road, the higher the extraction efficiency--therefore, it is very suitable for large-area road extraction, especially for large labeled data required for deep learning. The recall rate and IoU in the road extraction results for Data 2 and Data 4 are relatively low (Table 1). For Data 2, the reason is mainly that shorter roads were mistakenly deleted during vectorization and subsequent optimization. Moreover, this method mainly focuses on the optimization of road centerlines and the generation of road networks. Hence, as shown in Figure 18, auxiliary roads are not considered here. This is why the recall and IoU are relatively low. For Data 4, the width of the same level road is different, which is the main reason for the limited precision and IoU. We selected four tested images with different resolutions and obtained them from different remote-sensing platforms. The experiments on rural, suburban, urban, and rural residential area images proved the universality of the proposed method. Compared with the existing Gu's methods, the proposed method also showed better performance (Figure 17, Table 3). This can provide an accurate and complete way to extract roads at different scales, especially beneficial for the remote-sensing images of some areas with shadows and intersections. In addition, because of the wide universality, the proposed method has great potential in data labeling. We realize that the proposed method reduces the shadow effect; however, this proposed method still does not eliminate it (Figure 17b,e,h). The method also ignores auxiliary roads and is less effective when road widths are inconsistent, so further research efforts will be focused on refining the developed method. ## 5 Conclusions

In this study, a full-flow processing strategy including all steps from road extraction to road network generation is proposed, aiming at improving the efficiency of road network extraction and data labeling. To this end, a new framework with two main steps--single-road extraction and road network generation--is constructed by integrating various algorithms.