# City Information Modeling: State of the Art
## 3 CIM Research Areas
### Data Collection  
CIM is based on the 3D model of a city, containing the static attribute data (e.g., building structure type and height) and dynamic data (e.g., real-time traffic flow). Therefore, research studies on CIM data collection can be divided into collections of the 3D model, static attribute data, and dynamic data of the city. 1. Collection of 3D City Model  
The 3D city model is the foundational model of CIM that provides a refined geometrical expression of the 3D city space. Currently, satellite remote sensing, oblique photography, and light laser detection and ranging (LiDAR) are the major techniques applied to obtain raw data to reconstruct 3D models at the city level. These methods have different modeling principles and characteristics, including advantages and disadvantages in obtaining data, modeling scale, and modeling precision, as listed in Table 1.  
Table 1 shows that satellite remote sensing can be used for the large-scale, low-precision modeling of cities, whereas oblique photography is suitable for producing highly realistic and moderate-precision models. LiDAR can develop high-precision models with high costs and heavy workloads. Thus, it is generally utilized for modeling small areas in a city. Based on the modeling requirements (e.g., precision and area) of CIM, suitable 3D modeling methods for CIM can be determined from Table 1.  
2. Collection of Static Attribute Data of City  
The manual collection of the static attribute data of a city is time-consuming and laborious. Therefore, the following new techniques should be fully adopted to collect the static attribute data of a city for CIM. In terms of data sources, a variety of urban static data can be used to mine other related data, which is significantly valuable. For example, [PERSON] et al. (2018) established a modified static database to mine urban heat island data using satellite images and building information. In terms of algorithms, various machine learning algorithms can deduce vital information that is difficult to obtain directly ([PERSON] et al., 2018). For example, [PERSON] et al. (2018) presented a classifier based on support vector machines (SVMs) for semantic enrichment of coarse 3D city models by deriving the building type. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Principle** & **Characteristics** \\ \hline \multirow{4}{*}{Satellite remote sensing} & Detect and identify an object using sensing electromagnetic waves, visible light, and infrared rays reflected or radiated by the object within the distance. & Easy and economical to obtain the raw data, with a wide measurement range. Low precision. \\  & Obtain complete and accurate information of the ground objects photographed vertically or obliquely using the sensors on a flying platform. & Rapid modeling speed, moderate precision, and highly realistic 3D models with textures. \\  & & Difficult to identify and edit the targets in the model. \\  & Generate 3D models by transmitting detection signal to the target and comparing the received target echo with the transmitted signal. & High precision and suitable for super-fine or complex 3D models. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Principles and characteristics of main 3D modeling methods for cities ([PERSON] et al., 2018; [PERSON] et al., 2018). For computing, cloud computing can process large amounts of data within a short time, enhancing the data collection for CIM [24; 25; 26]. [PERSON] et al. [27] established a machine learning model to predict the structural types of buildings based on cloud computing. This model can collect data on the structural type for building groups in a large city and further enrich the CIM model. Collection of Dynamic Data of City  
The dynamic data of the CIM can be obtained using IoT technology. Previous related research [28; 29; 30; 31] can be divided into perception, communication, and application. IoT-based perception collects the status-quo information for CIM, such as traffic, weather, and crowds.