## 1 Introduction  
Urbanization is an inevitable part of development in countries all over the world. Expanding cities have led to a considerable increase in built-up areas [1], intensifying environmental degradation, global warming, and the urban heat island effect [2, 3]. Buildings and urban structures also influence environmental conditions, a critical factor determining the quality of life in metropolitan areas [4]. High building density and a lack of control over built-up environment parameters, such as the building coverage ratio (BCR) and the floor area ratio (FAR), is exacerbating urbanization issues such as urban sprawl [5], the deprivation of the right to sunlight, and a lack of sufficient infrastructure provision [6]. Accurate insights into those parameters (e.g., BCR and FAR) and their relationship with the urban condition is crucial for urban analysis to mitigate the negative consequences of urbanization and, therefore, vital for urban status monitoring and analysis [7]. Understanding the BCR parameter contributes to better planning decisions as it plays a central role in a wide range of urban phenomena [8]. The BCR impacts ground surface reflectivity, airflow, and heat transmission in the atmosphere over metropolitan areas, causing Urban Heat Islands (UHI) [9]. Scholars indicate that controlling the BCR may help reduce this phenomenon [8, 10]. Moreover, choosing an appropriate BCR for urban planning and building design may be an effective strategy to address difficulties created by urbanization, and to enhance both environmental conditions and citizens' quality of life. For instance, in the context of urban management, the maximum (or \"legal\") BCR and FAR limits are fundamental tools for district-scale zoning regulation that legally control the shape and volume of buildings, supporting more effective urban planning as well as improving city aesthetics [11]. However, BCR violations, such as encroachment construction, have become a major problem for urban management in several areas [12]. Although an effective BCR monitoring tool will support the efforts of city planners to enforce existing urban management and planning regulations [13], it has yet to be achieved. Due to the tendency toward urban sprawl, cities are dynamic, continually expanding, and changing dramatically, which is especially the case in developing countries [14; 15]. This makes extracting urban features such as the BCR even more challenging, and many urban communities lack access to a comprehensive BCR dataset for their area [16]. Recent advances in Deep Learning (DL) have had a significant impact on fields such as spatial analysis and remote sensing. DL is a type of Machine Learning (ML) that uses multiple layers of neurons with complicated structures, or applies non-linear changes to high-level model abstractions of the original input [17]. Thanks to DL, extracting urban features from high-resolution spatial images is now becoming more accessible and faster [18; 19; 20]. Several state-of-the-art detection models are now available that deliver superior performance for building footprint extraction [21; 22; 23]. The building footprint can be used to describe the share of the building area in the total block area, which is crucial in the BCR measuring [24]. Therefore, automated building footprint extraction based on DL has the potential to speed up the BCR calculation process considerably, compared to manual mapping methods. In the literature review, we summarize the advantages and shortfalls of proposed BCR calculation methods. Due to the limitations of conventional and statistical-based approaches, the demand for a quick and robust BCR measuring approach was proven. Most previous BCR calculation methods have required a pre-processing phase to create the desired input, after which the calculation process can proceed. This makes the process both protracted and complex. The use of DL should make this pre-processing step simpler, or even unnecessary, due to the automatic detecting and generating of the building footprint outlines.