## 5 Conclusions  
The advancements of ML and DL accelerate the proposal of robust urban assessment tools. However, utilizing DL algorithms to optimize the BCR calculation has yet to be focused upon. Therefore, this paper suggests a BCR calculation method based on the deep learning (DL) workflow in ArcGIS. The accuracy achieved by extracting the BCR from drone images based on DL was evaluated by analyzing the BCR values of 116 blocks and 267 parcels. This method is quicker and more effective than conventional methods, and it also overcomes the limitations of the previously proposed methods in terms of distinguishing buildings and preprocessing data. The building footprints were extracted automatically, resulting in a considerably reduced process duration. Furthermore, the instance segmentation algorithm identified each building as a distinct entity, so that the BCR measured was more accurate, especially in dense areas. The results indicate that the accuracy of the DL-based method was considerable, with average and median accuracies at the block scale of 86.55% and 94.07%, and at the parcel scale of 85.21% and 94.29%, respectively. However, the accuracy of this approach relies heavily on the DL performance, which is affected by both the quality of the input data and the training process.  
Based on the accuracy evaluation, we conclude that the DL-based approach is suitable for BCR measuring, which is a stepping stone to proposing an implication of this method. Techniques based on deep learning may aid in the monitoring of the BCR in urban management due to the robustness of DL and drone mobility. This approach has the potential to assist policymakers to analyze a city's building coverage and identify legal BCR compliance, thereby contributing to the prevention of encroachment construction and the control of urban density distribution. In equatorial or warm climatic regions where the urban heat island effect is more severe, urban density control is a workable strategy to mitigate negative effect this urbanization phenomenon.  
The main limitations of this study are as follows. First, due to the lack of public datasets, the model was trained with a mixed-training dataset, which is not an ideal condition. The training dataset establishment needs to be improved so that better proven the generalization abilities of the model. Second, our proposed DL-based method was assessed for industrial zones, where the building type is uniform and lacks building-height diversity. Further research should consider the issue when assessing high buildings to find a way to deliver more reliable results. Besides, the cost-benefit of this method also needs to be evaluated in further investigations to clarify the economic factor for sustainability in social aspects.  
Conceptualization, Q.H.L. and N.K.; methodology, Q.H.L., H.S. and J.H.; software, Q.H.L., H.S. and J.H.; visualization, Q.H.L.; writing--original draft, Q.H.L.; writing--review and editing, Q.H.L., H.S., N.K. and Y.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22 CTAP-C163903-02) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20202020800030, Development of Smart Hybrid Envelope Systems for Zero Energy Buildings through Holistic Performance Test and Evaluation Methods and Fields Verifications).  
Not applicable.  
The authors declare no conflict of interest.