## 2 Literature Review  
As the review demonstrates in Table 1, a great deal of effort has been devoted to finding a good way to measure urban density and building coverage in recent decades. In order to shed light on the relationship between the BCR and land-use change, [PERSON] et al. [12] carried out a field study to collect evidence. However, site surveys are labor-intensive, time-consuming, and have limited utility. As the drawbacks of census and site survey methods are significant, BCR datasets generated by sources such as satellite and airborne imagery could be attractive alternatives. In a study that measured variations in building density by using the BCR and the FAR indices, [PERSON] et al. [25] proposed a statistical method to measure the BCR from satellite imagery using ArcMap and ENVI to obtain building heights and coverage areas. For this statistical analysis, building contour retracing was manually repeated to compute areas. Although this building information extraction technique was challenging or incorrect when dealing with complex building geometries, this method gives a quantitative strategy to monitoring building density distribution. In response to manual mapping challenges, researchers have developed an automatic approach to extract urban density measurements from polarimetric synthetic aperture radar (SAR) imagery. [PERSON] and [PERSON] [26] proposed novel urban compactness estimatingmethod based on a polarization orientation angle (POA) algorithm. However, a subsequent study by [PERSON] et al. [27] pointed out a number of issues with this method that limited the utility of the urban density maps generated that they addressed by extending the POA algorithm to develop an alternative method for estimating urban density from SAR images. Other research has focused on using digital surface model (DSM) and LiDAR data. For example, [PERSON] et al. [28] proposed an automated approach to assess building geometry information. They used a progressive triangular irregular network (TIN) to detect buildings and generate building outlines to extract parameters, such as the BCR and the FAR. Although LiDAR data, satellite, and SAR imagery are all good sources for extracting urban features, they are not always available or affordable. Low-cost alternatives, such as Open Street Map (OSM), have also been pressed into service to analyze building compactness [29]. However, open data sources and OSM data vary from region to region, especially in developing countries, making the widespread use of OSM data for urban analysis somewhat problematic. Unlike satellite imagery, airborne images are much more accessible. For example, [PERSON] et al. [30] proposed a method for computing the BCR and the FAR from airborne images and DSM based on the mean-shift algorithm. The effectiveness of this approach is significant due to its utility in cases where only limited information or low spatial resolution data are available. However, the mean-shift-based method is less suitable for congested areas since buildings are detected as homogeneous objects, reducing model accuracy. By considering the importance of the geographic distribution of building coverage for sustainable development, [PERSON] et al. [16] developed a model for estimating building coverage and predicted the BCR distribution across the contiguous United States. This study provided quantitative information on the geographical distribution of building coverage at the national scale and may thus only be appropriate for large area urban analyses.