# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 1 Introduction  
Population growth is one of the five major problems facing the world today [1]. It is also an important strategic problem in China during this century. Population growth is closely related to national economic development, social stability, and living standards. Population data, especially urban population data at the fine-scale, is the basic statistical data related to national economies and people's livelihoods. It can provide guidance regarding economic development, social governance, and environmental protection [2]. Accurately estimating the population at a subdistrict scale in a city is important for upgrading the urban management level, optimizing the allocation of resources, and accelerating social management [3]. Thus, it is a requirement for fine-scale urban management in the context of rapid urbanization. Population data in China is mostly obtained with statisticalmethods, so it is scientific and accurate [4]. However, this traditional census approach has the disadvantages of high costs, temporal delay, and insufficient spatial detail [5; 6]. Population estimation is a hot topic attracting researchers from different domains, including demography [7], video analysis [8], and geography [9]. Based on the mobility of the population, population estimation is divided into two categories: static population estimation and dynamic population estimation. The core idea of dynamic population estimation is to use human mobility data to estimate the fine-scale dynamic population distribution [10]. Existing data sources used for dynamic population estimation research mainly include mobile phone signaling data, social media big data, subway card data, etc., [11; 12; 13; 14; 15]. Considering the dynamic changes of the human population is important when designing traffic assignment plans, evacuation procedures, and shelter locations [16; 17]. The static population is the permanent population of a certain area. Different from the variability of dynamic population, static population has certain stability. Static population estimation is helpful to regional energy resource allocation, urban planning, social governance, and environmental protection [18]. Based on the data sources and mathematical methods employed, the methods used for static population estimation can be divided into the following three categories: traditional population prediction methods, areal interpolation, and methods based on statistical models [19]. Traditional population prediction methods include the average growth rate method, dependent coefficient method, and Pierce model. These methods generally establish an estimation formula, by using population data from several consecutive years [20]. This type of method is usually applied over a large research area and the calculation method is relatively simple, and thus it is not suitable to meet the needs of subdistrict-scale population estimation. Using mathematical methods to interpolate census data, areal interpolation converts the population data in the geographic space [21]. This type of method can solve the problem of census data with insufficient spatial resolution to some extent, but it is difficult to improve the time resolution. With the advent of the era of big data and the rise of dynamic data acquisition technology, large volumes of data related to human settlements are generated, such as remote sensing images, the traffic bayonet-corpus, trajectory data, signaling data, and point of interest (_POI_) data [22]. Population estimation models can be established with statistical methods, by analyzing the mathematical relationships between the population size and statistical variables that might affect populations. Based on Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night lighting data, lighting indexes, and historical population data were previously employed to predict urban populations [23; 24; 25; 26].