# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 3 Results and Discussion  
### Selection of the Optimal Algorithm  
The first procedure was conducted to select the best-performing algorithm. Drone images are typically prepared for this purpose, as shown in Figure 5. Jeju's urban area has a high concentration of buildings. When using the RTK GPS, the resulting images were highly accurate and large, with a precision of 5 cm. Figure 5a displays the entire region, with an image size of 500 \(\times\) 500 m\({}^{2}\). Figure 5b illustrates the image after it was segmented into 10 \(\times\) 10 smaller images, resulting in a total of 100 images. In Figure 5c, the map was manually labeled by humans, with only the roofs highlighted in black, which were later categorized under the roof class. Figure 5d shows the corresponding 100 labels for the segmented images. The label images created manually in this manner had two classes: ground and roof. The aim was to find the best-performing algorithm through training and testing without validation, using the same options whenever possible. Therefore, separate validation was not conducted. Figure 5: The data used for training: (**a**) drone-captured images divided into 100 images and (**b**) divided images with dimensions of 256 \(\times\) 256 \(\times\) 3. (**a**) Labeled images corresponding to one image and (**c**,**d**) the divided labeled images. Figure 6 presents the test images produced in a consistent manner. Figure 6a displays the entire test image. Figure 6b illustrates the 100 segmented images derived from the main image. Figure 6c depicts the comprehensive label image, while Figure 6d showcases the 100 individual images that were manually labeled. For testing purposes, segmented images were utilized. Notably, these images include the latitude and longitude details for each pixel. A program was developed incorporating algorithm verification, data refinement, and GIS DB update functionalities using the MATLAB 2022b library to facilitate the proposed process. To determine the most effective performance of Unet, SegNet, ResNet18, and ResNet50, tests were conducted on an Intel(R) Core(TM) i9-10900 and RTX 3070 8 Mega. For all algorithms, the number of epochs and batch size were set to 50 and 10, respectively. The learning time was measured and compared among the different algorithms to select the algorithm that required the least amount of time. The results are shown in Table 1. ResNet19 had the shortest learning time among all models. After measuring the learning time for U-Net, SegNet, ResNet18, and ResNet50, their performances were evaluated for roof detection accuracy. The total accuracy, mean accuracy, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **UNet** & **SegNet** & **ResNet18** & **ResNet50** \\ \hline Training time & 220.9 & 170.3 & 85.3 & 174.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Elapsed time. Figure 6: Data used for testing. (**a**) Drone-captured images divided into 100 images and (**b**) divided images with dimensions of 256 \(\times\) 256 \(\times\) 3. (**c**) Labeled images corresponding to each divided image and (**d**) divided labeled images. Images contain latitude and longitude information for each pixel. mean IoU, and weighted IoU were measured and are listed in Table 2. Although ResNet50 exhibited an accuracy of over 80%, it required a long training time. Therefore, ResNet18 was chosen for this test because of its relatively high accuracy and short training time. The training was conducted using the same dataset and for the same number of epochs across all models. The respective layer counts for UNet, SegNet, ResNet18, and ResNet50 were 58, 59, 100, and 206. Their training speeds were recorded as 220.9, 170.3, 85.3, and 174.5 respectively. It seems that ResNet50, with its most complex layer structure, took the longest training time and achieved the highest accuracy. Given that accuracy is more crucial than time, memory, or resources in our context, ResNet50 appeared to be the most suitable algorithm.