# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 3 Results and Discussion
### Refinement of the GIS-Labeled Dataset  
The intention was to conduct data refinement using Resnet50, which had the best performance. Manually labeling roofs is time-consuming, so there is a need to automatically label using the GIS of the building registration ledger. As mentioned earlier, once the top-performing algorithm was selected, the aim was to refine the GIS-labeled training dataset using this algorithm. After undergoing this refinement process, the performance of the neural network could be enhanced. The training was conducted using GIS labeling from two urban areas. To test the trained network, a third city was selected for evaluation. Figure 9 displays the study's focus on urban areas. Figure 9a,b present the targeted urban regions, while the corresponding GIS system labeling information is illustrated in  
Figure 8: (**a**) Actual site image and (**b**) results of (1) Unet, (2) SegNet, (3) Resnet18, (4) and Resnet50, respectively.  
Figure 7: Misclassified regions of ResNet50: (**a**) false negatives and (**b**) false positives.  
Figures 9c and 9d, respectively. Furthermore, with the aim of refining the data, a validation procedure that adjusts separate options was not established. The primary objective of the training here was data refinement. As seen in the figure, by using the GIS, the building registry contained latitude and longitude coordinates, allowing it to be utilized as labeling data that matched the coordinates in the image.  
Figure 10 displays the images used for training. Figure 10a is the initial training image with its corresponding label shown in Figure 10b, and the similarity score for the network regarding this image is 0.2944. Similarly, Figure 10c presents another training image with its label illustrated in Figure 10d, and it has a similarity score of 0.4541. By removing these two images and measuring the test results, improved performance was observed when comparing before and after refinement. The table displays performance metrics before and after manual refinement. Direct human intervention identified and removed 10 instances with the lowest similarity out of the initial 200 data points. By excluding these potential sources of error, improved results were achieved. Such hands-on refinement is vital to curtail error propagation, ensuring the model's robustness in real-world applications. Through this refinement process, it was confirmed that performance can be enhanced by cleansing the input data. In this manner, it was verified that the network performance can be improved, as shown in Table 4, by training and refining using the GIS building information system without manual labeling by humans. However, compared to manually labeled data, the performance using ResNet50 was found to be inferior. Yet, there is a  
Figure 9: For data refinement, images in (**a,c**) were divided into 100 images and used as input for the training network, while (**b,d**) were used as the target for the network with 100 images.  
significant advantage in being able to utilize GIS information without manual labeling. Regarding _IoU_, it is  
\[IoU=\frac{TP}{TP+FP+FN}\]