## 4 Results and Discussion  
### Results  
The fine-tuned model can precisely generate the building footprints for buildings in the reference area. The comparison between segmentation results and the ground truth label indicated the model can also distinguish buildings that are close to each other by generating masks in different colors (Figure 5a). Since each building is identified as a distinct entity, the BCR calculation in dense areas is more accurate. In Figure 5b, results after the application of the fine-tuned model to generate building footprints from drone images taken in Zone B, used for the BCR calculation, are shown. The BCR was calculated for each parcel and block in the referenced area based on the extracted building footprints. Then, the result was compared with the actual BCRs from the ground truth dataset to validate the accuracy of DL-based method. In order to identify the correlation between the two variables, the BCRs at both the block scale and parcel scale were plotted on scatter charts to highlight the relationship (Figure 6a,b). The R\({}^{2}\) values off 0.5899 and 0.7284, respectively, for the block scale and the parcel scale, although there are some outlier points where a calculated BCR value is either much larger or much smaller than that for the actual data. The outliers in the BCRs at the block scale were significantly greater than those at the parcel scale, leading to a considerably lower R\({}^{2}\) value. The outliers were mainly located in the low-density blocks with building densities below 20%; the fitting for the dense blocks was generally much better. Unlike the block scale, the pattern in the parcel scale data was less clear, with outliers appearing at many levels of the BCR, including in the range from 20% to 60%. Displaying the results as histogram charts (Figure 6c,d) revealed that a large number of samples had accuracies of over 85%. At both scales, the accuracy from 90% to 100% accounted for the most significant proportion of the results. The number of samples with an accuracy lower than 50% was considerable at the parcel scale, where 22 samples accounted for 8.24% of the total, while at the block scale just seven samples were below 50%, accounting for 6.04% of the total. The accuracy of DL-based methods is shown in Table 4. At the block scale, the presence of even a few outliers reduced the average accuracy to 86.55%, even though the median was 94.07%. The median, which is the middle value in a BCR accuracy list ordered from smallest to largest, indicates that half of the samples were over 94.07%. The gap between average accuracy and median accuracy at the block scale was narrower than at the parcel scale, which suggests that the performance of our proposed DL-based method was more stable at a larger scale, where a lower level of detail is required. For the purpose of gaining insight into the status of the building coverage ratio in the referenced area, the extracted BCR based on DL for the block and parcel scales is shown in scatter plots (Figure 7). Here, the coverage ratios between 60% and 70% accounted for a large proportion at the block scale, indicating that the study area is quite congested. The proportion of blocks with a coverage ratio lower than 40% was relatively small, and just a few blocks had BCR values higher than 75%. The coverage ratio distribution at the parcel scale was wider, ranging from 50% to 80%. Besides, a quantitative and visual view of the BCR distribution is achieved by using a thematic map, as shown in Figure 7c,d. The large blocks, which are usually parks, have lower levels of BCR than small blocks. Due to the construction of buildings, the coverage ratio is usually high in parcels that have a small area. Some of them have a coverage ratio of up to 80%, which is reaching the maximum legal BCR. This reflects the status of construction in a crowded industrial zone like Sihwa.