Urban built environment is the manufactured environment where human beings live. The stocks of the urban built environment refer to the quality of materials (e.g., concrete, steel, copper, etc.) that accumulated in buildings and infrastructure. Revealing the spatial distribution of urban built environment stocks arises as a new direction for digital city construction, which helps to understand the urban development patterns and urban resource and waste management. Developing an urban circular economy and realizing sustainable urban development is essential. Therefore, it is necessary to summarize and sort out the current spatial calculation method of built environment stocks.This study introduces a detailed theoretical basis and development status of three methods for spatial calculation of urban built environment stock: that are the top-down method, the bottom-up method, and the remote sensing calculation method. The advantages and limitations of these models are elaborated with application and data availability. The top-down approach has a complete set of theoretical foundations and algorithm models, which can perform large-scale material flow analysis well. Due to its inability to obtain a high spatial resolution, this method is not suitable for analyzing urban development within cities. Contrastingly, the bottom-up method permits fine-grained stock estimation by gathering cadastral-level physical measurements of buildings and infrastructure and associated material composition indicators. However, it is labour-intensive and the scope of the bottom-up method is often restricted to city-level or lower geographical regions. As for remote sensing calculation, previous studies established a linear regression relationship between the nighttime light radiation intensity and the built environment stocks in the study areas. However, the night light remote sensing data will degrade the reliability of quantitative analysis due to background noise and radiation saturation effect. Thus, stock data with the high spatial resolution are impossible to acquire. These three traditional methods are often difficult to strike a balance between large scale and high spatial resolution. However, in the era of big geographic data, more data sources have brought new research directions for stock calculation.Geo Big Data and Earth Observation data are essential in developing earth science, environmental science, remote sensing science, and geographic information science. Combining these wide-coverage, high-precision, and fast-update data and machine learning methods have been widely used in poverty surveys and energy consumption. This paper proposes a framework that combines big geographic data and machine learning for stock calculation based on the above background. We expect an end-to-end method to estimate grid stocks directly from publicly available information that minimizes manual involvement. However, the heterogeneity of geospatial and the black-box nature of deep learning may have an impact on the migration effects of the model. Despite its drawbacks, this migration model has the potential for large-scale, high-resolution stock calculation in future works.