###### Abstract
[PERSON] used cab trajectory data and real-time Tencent user density data to analyse human activity patterns and spatial distribution characteristics. The precision of identifying building functions was increased by analysing spatial interactions [23]. [PERSON] proposed a method that integrates feature decomposition with k-means clustering to infer building functions according to location-based Tencent user density (TUD) data [24]. [PERSON] proposed a content-first social media image dataset filtering method to extract the geospatial information of ground-level buildings from more than 28 million images. This can categorise buildings into commercial, residential, and other types [25]. [PERSON] utilised telecommunication traffic data to group urban buildings and then used planning documents and POIs to identify building functions to infer 'high-frequency' urban functions [2]. The use of social perception data to recognise building functions has become a hotspot of research. However, the location accuracy of data from social media, mobile communications and rental car tracking is low. Furthermore, the service is expensive and the data are not easily accessible. POI data originating from Internet maps have the advantages of being information-rich, time-sensitive, and easy to access. POI data have high location accuracy and contain information about the types of geospatial entities, which is advantageous in recognising the functions of urban buildings [26]. More and more researchers are using data provided by web maps to identify building functions [27; 28]. Identifying building function using POI data relies heavily on the density values of POI types within the building contour [29]. [PERSON] estimated the functional types of OpenStreetMap building footprint data based on urban morphology analysis, and was able to accurately differentiate between residential buildings, industrial buildings, and accessory buildings [30]. [PERSON] conducted a study on the functional classification of urban buildings using POI and high-resolution remote sensing imagery [29]. [PERSON] used machine learning to analyse shape, size, and location features from OpenStreetMap building data to predict whether residential buildings can be sprayed with insecticides to help prevent malaria [31]. [PERSON] used POI data to classify building functions by improving the indicator frequency density ratio method commonly used in the identification of urban functional areas. This approach solves the problem of identifying building functions in POI-sparse areas by expanding the POI search range and applying inverse distance weighting to the indicator frequency density ratio [32]. [PERSON] et al. collected POI data, building coverage area data, and road information from the Baidu and Gaode maps. This was combined with land use data to develop an NLP-based method for identifying urban building types in Beijing, China. It improves the low identification accuracy of buildings that do not contain POI points and utilises distance metrics to estimate building functional types [33]. [PERSON] et al. proposed a hierarchical data mining model to identify urban building functions. It combines Gaode map building footprints, POIs, land survey data, and high-resolution remote sensing imagery to solve the missing data problem of some residential POIs [34]. [PERSON] et al. proposed a machine learning classification algorithm based on OpenStreetMap (OSM) building data to categorise buildings into three types: single-family residential, multi-family residential, and non-residential [35]. [PERSON] et al. proposed a step-by-step identification method for urban building functions based on remote sensing imagery and POI data, which integrates the spatial similarity of buildings and the kernel density of POIs to improve the accuracy and completeness of identification [36]. In summary, the use of POIs and building outline data from online maps to recognise urban building functions provides high building outline accuracy and rich semantic information on POIs.