# 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
Among the four data, Data 1 was located in a rural area, with the simplest road conditions and the highest resolution. Therefore, the extraction result is very similar to the ground truth (Figure 16a,e). Data 2 is more complex than Data 1, but each road is also extracted relatively completely (Figure 16b,f). Because of many details in Data 2, the recall rate and IoU in the road extraction results are relatively low (Table 1). Data 3 is located in the city, and the road situation is the most complex. The results of Data 3 have the highest precision but the lowest accuracy (Table 1), meaning that most parts of roads are identified accurately but the differences between roads and surroundings are not distinguished well. Data 4 is located in a rural residential area with multiple roads of varying lengths and widths. The proposed method can extract the roads in the image very well by extracting the roads by grading, that is, firstly extracting the wide roads, and then extracting the narrow roads (Figure 16e,h). However, the width of the road at the same level in the image is also different, which makes the proposed method obtain limited extraction precision and IoU (Table 1). Moreover, all road intersections in the four data are correctly connected. Figure 3:8 Results of different simplifying thresholds: (**a**) tracking result; (**b**) result when k = 1; (**c**) result when k = 3; (**d**) result when k = 5. ### Comparison with Other Existing Methods

We make comparisons with [PERSON]'s road extraction method [41] in this study. [PERSON]'s method is implemented in the following way: first determine a seed point to obtain the initial contour through region growth (use the same threshold as the developed method), and then use the GVF-Snake method to perform iterative optimization to obtain the road boundary, where the number of iterations is 40. [PERSON]'s method has many iterations, so it \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Precision** & **Accuracy** & **Recall** & **IoU** \\ \hline Data 1 & 88.54\% & 99.70\% & 88.97\% & 0.80 \\ Data 2 & 87.08\% & 98.13\% & 77.06\% & 0.69 \\ Data 3 & 98.10\% & 88.31\% & 88.68\% & 0.87 \\ Data 4 & 80.70\% & 96.80\% & 81.56\% & 0.68 \\ \hline \hline \end{tabular}

Ten roads in Data 2 were randomly selected for statistical analysis of extraction time (Table 2). Roads with a length of more than 4000 pixels can be extracted at one time in 7 s. In terms of extraction efficiency, the longer the road, the shorter the road extraction time per 1000 pixels. The average extraction time of roads per 1000 pixels is 1.81 s.

\end{table} 
Table 1: Evaluation table of road extraction. Figure 16: Comparison between the road extraction results and the ground truth: (**a–d**) are the road extraction results and (**e–h**) are the ground truth. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Road Length (pixel)** & **Time (s)** & **Time per 1000 pixels (s)** \\ \hline
1 & 2040 & 4.125 & 2.02 \\
2 & 2126 & 4.739 & 2.23 \\
3 & 2378 & 4.64 & 1.95 \\
4 & 2415 & 5.688 & 2.36 \\
5 & 2770 & 6.776 & 2.45 \\
6 & 4056 & 4.536 & 1.12 \\
7 & 4064 & 6.43 & 1.58 \\
8 & 4115 & 5.83 & 1.42 \\
9 & 4121 & 6.095 & 1.48 \\
10 & 4188 & 6.486 & 1.55 \\ Mean & - & - & 1.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation table of road extraction efficiency. is only suitable for road extraction in local areas. Three local typical roads were selected for comparison. For each road segment about the length of 300 pixels, the method takes around 2 s. This is less efficient than the developed method (Table 2). Figure 16 shows the extraction results of the two methods. For Sample 1 and Sample 2, the recall of [PERSON]'s method is higher than ours. However, the precision and IoU of the developed method are all better than those of [PERSON]'s method (Table 3). As shown in Figure 17a,d, [PERSON]'s method could distinguish wild country and artificial structures exposed to the sun, including roads, which led to high recall.