Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China

Abstract

The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.83 and 0.71, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.

1. Introduction

Buildings are a vital element in urban studies. As the fundamental structural element in the urban physical space [1], buildings are the basic spatial unit to monitor urban structure development in the horizontal and vertical dimensions. The product of urban 3D building structure, i.e., building footprint, height, and volume, is a proxy for analyzing structural specifics within cities and revealing their driving factors [2]. As the main venues of urban activities in the urban socioeconomic space, buildings are the basic measurement unit to study the impact of human activities in the process of quantitative urban modeling. The product of building type can be a proxy for analyzing urban socioeconomic features dominated by human activities (e.g., living, working, and recreation), which are represented by the building types. For example, in a bottom-up urban building energy use model developed by Li et al. [3], the information of building type is a key input for capturing spatial and temporal patterns of energy consumption for different types of buildings.

Although building type is one of the key input variables in quantitative urban studies [4,5,6], this information is largely available in the shared local governmental database (e.g., accessor’s parcel geodatabase). In countries without accurate and up-to-date governmental geodatabase, time-consuming and labor-intensive field surveys remain the main approach to obtain building type information [1]. To address this problem, recent studies have investigated the potential of remote sensing and social sensing data to map building type. Remote sensing data can be used to retrieve building type information (e.g., residential and commercial buildings) at large scales based on their physical features (e.g., textural, spectral, and structural) derived from high-resolution optical images [7,8,9], stereo optical images [10], and light detection and ranging (LiDAR) data [1]. However, their ability to identify detailed building types with similar spatial forms (e.g., hospital, hotel, and restaurant) is limited. In contrast, social sensing data (e.g., social media data and taxi trajectory data) can be used to derive detailed building type information. This is because social sensing data were collected from individuals and thus, they can capture different patterns of human activities at a fine scale [11]. By revealing the relationship between building types and spatiotemporal features of human activities, social sensing data were utilized to further identify building types with similar spatial forms. However, due to the limited availability of data records, most studies using social sensing data were conducted at local and regional scales, especially in big cities [12,13,14], where people are prone to take a taxi and use social media more frequently compared to living in small cities [6].

Fortunately, geospatial data from the web mapping platform (e.g., OpenStreetMap, Baidu Maps (https://map.baidu.com/), and Gaode Maps (https://www.amap.com/)), including building footprints [15], land use polygons, roads, and point-of-interest (POI) [16], offer an opportunity to map detailed building types over a large scale. These data are sufficient for building types mapping with its copiousness, large area coverage and reliability [17,18,19]. To meet the increasing demands for mobile navigation service in human’s daily life, web mapping platforms frequently update geospatial data, even in small cities. For instance, Baidu Maps has over 300 million active users and billions navigation service requests in China each day on average [17]. By virtue of this property, many studies utilized building footprints from web mapping platforms to map 3D building structure at a continental-scale [2] and utilized POI data from web mapping platforms to map urban functional regions [20] and population density [21] at a national-scale. POI is a specific point location that can be used to represent a venue (e.g., hotel and restaurant) in the physical world, with various attributes, such as, the name, address, coordinates, and category [22]. Among these attributes, the category attribute is cognized and conceptualized [8], and there are a growing body of literatures, in which this attribute was linked to urban function types for urban land use mapping [23,24,25,26,27,28]. Thus, in this study, the category attribute of POI data was used to infer detailed building types surrounding POIs.

However, the inconsistency between the POI categories defined by web mapping platforms and categories required in urban studies limits the use of POI data. For instance, gates and building numbers categories are defined by web mapping platforms to help users precisely find their desired locations within large industrial/commercial/residential region. In this occasion, researchers cannot link these POI categories to any human activities and therefore most of the POI data studies [21,23,29] always discard this part of POIs when they manually link POI categories to the human activity categories that they needed in urban studies. However, the human activity information of these POIs can be inferred under the help of the name attribute. This attribute in POI data is described by the natural language, so the human activities information can be extracted via natural language processing (NLP) techniques.

There are challenges in mapping building types. First, there are limitations to identify detailed building types using remote sensing data, while there are challenges to map building types over large areas using social sensing data. Second, POI categories may not be able to be linked directly to human activities, and additional processes are needed to derive the information of building types using POIs. In this study we aimed to address these challenges in two major areas. First, we developed NLP-based approaches to identify corresponding building types hidden in the name attribute of POIs. Second, we collected geospatial data from Gaode Maps and Baidu Maps and proposed new ratio-based approaches to derive the urban building types in Beijing, China. The remainder of this paper describes the study area and dataset used in this study (Section 2), the proposed POI categories reclassification approach (Section 3.1) and building type identification approach (Section 3.2), results (Section 4), discussion (Section 5), and concluding remarks (Section 6).

2. Study Area and Data

As the capital of China, Beijing is located in the north margin of the North China Plain (Figure 1). Along with rapid urbanization, this megacity in 2018 (696.63 km²) increased by approximately 30 times compared to the area in 1980 (23.53 km²) [30]. Meanwhile its ring road network gradually expanded to 6^th Ring Road during the past 30 years. As the core area of economic development of Beijing, the area within the 6^th Ring Road has a high density of buildings and a diversity of building types. Therefore, this city can serve as an ideal region for deriving building type information.

The geospatial data used in this study include POIs, building footprints, land use parcels, and road networks. Baidu Maps and Gaode Maps are the two most popular web mapping platforms in China for collecting geospatial data (Figure 2A,B) to provide navigation services. We used web crawlers to collect POIs in Gaode Maps. A total of 1,455,461 POIs in 2019 were acquired via the application programming interface (API) provided by Gaode Maps, and these POIs can be grouped into the 23 big categories (e.g., food and beverages, shopping, daily life service, sports and recreation, medical service, accommodation service, tourist attraction, commercial house, governmental organization and social group, science/culture and education service, place name and address, and pass facilities). Its initial three-level category scheme can be found in its document (https://lbs.amap.com/api/webservice/download) and the 23 Level I categories were further classified into 264 Level II categories and 870 Level III categories.

We used web crawlers to collect roads, building footprints, and land use parcels from Baidu Maps. A total of 440,781 building footprints, 48,452 land use parcels (e.g., parks, companies, hotels, residential districts, greenspaces, parking lots, and schools), and 32,563 roads were acquired from Baidu Maps in 2019. The land use parcels and roads have a code attribute (Figure 2C) to infer land use types and road network levels, respectively.

3. Methodology

We developed a stepwise method to identify urban building types including residence, office, school, retail, hotel, restaurant, and hospital in Beijing, China as shown in Figure 3. First, the initial POI categories from Gaode Maps were reclassified to which can be used to directly infer building types by using a lookup table (Figure 4C). Then, NLP-based approaches (i.e., text similarity measurement and topic modeling) were developed to reclassify POI categories that cannot be reclassified by the lookup table (Figure 4D). Second, land use parcels were used to identify buildings types within parcels. Then, for buildings outside parcels, ratio-based approaches (i.e., type ratio and area ratio) were developed to identify buildings types with/without POIs nearby. More details about each step are presented in the following sections.

3.1. Reclassification of POI Categories

We built a lookup table (

Table 1. The lookup table for reclassifying POI categories into corresponding building types.

POI Categories from Gaode Maps			Reclassified POI Category (Building Type)
Level I	Level II	Level III
Auto Service	All		Retail
Auto Dealers	All		Retail
Auto Repair	All		Retail
Motorcycle Service	All		Retail
Sports and Recreation	All		Retail
Daily Life Service	All		Retail
Shopping	All		Retail
Medical Service	Pharmacy	All	Retail
	Veterinary Hospital	All	Retail
	Others	All	Hospital
Food and Beverages	All		Restaurant
Accommodation Service	All		Hotel
Science/Culture and Education Service	School	All	School
	Others	All	Office
Tourist Attraction	All		Office
Governmental Organization and Social Group	All		Office
Commercial House	Industrial Park	All	Office
	Building	Industrial Building	Office
		Business Office Building	Office
		Others	Residence
Transportation Service	Airport Related	All	Office
	Railway Station	All	Office
	Port and Marina	All	Office
	Coach Station	All	Office
	Border Crossing	All	Office
	Others	All	Unrelated
Road Furniture	All		Unrelated
Finance and Insurance Service	ATM	All	Unrelated
	Others	All	Office
Enterprises	All		Office
Place Name and Address	Address Sign	Building Number	Unclassified
		Others	Unrelated
	Others	All	Unrelated
Pass Facilities	Gate of buildings	All	Unclassified
	Gate of Street House	All	Unclassified
	Virtual Gate	All	Unrelated
Public Facility	All		Unrelated
Incidents and Events	All		Unrelated
Indoor facilities	All		Unrelated

) to reclassify POI categories into corresponding building types. The category of ‘Unrelated’, accounting about 9.7% of total POIs, means POIs with this category are unrelated to buildings and were removed in our analysis. The category of ‘Unclassified’, accounting about 15.8% of total POIs (Figure 4A, B), means POIs with this category are needed to be reclassified into corresponding building types using the NLP-based approaches because ‘Unclassified’ POIs include those related to building numbers and gates and their initial categories cannot be used to directly infer building types. Via the text similarity measurement and topic modeling for POI names, we can identify their corresponding building types. In the rest of paper, we called POIs with corresponding building types in Table 1 as ‘Reclassified’ POIs and called POIs without corresponding building types in Table 1 as ‘Unclassified’ POIs.

3.1.1. Text Similarity Measurement

The text similarity measurement was used to identify the corresponding building types of ‘Unclassified’ POIs. This new approach was designed based on a special naming rule of ‘Unclassified’ POIs from web mapping platforms. ‘Unclassified’ POIs were collected by web mapping platforms to help users find their desired locations within a large region. For example, there is a large industrial district in Figure 5 with a POI named ‘Kangsheng Industrial District’. Two gates and eight buildings POIs are within this industrial district, and their names have same prefix of the name of industrial district POI. Their corresponding building types can be inferred by the reclassified category of industrial district POI. Three steps of the text similarity measurement using this rule are described in details below (Figure 6).

First, a total of 45 analysis units were generated using roads with ‘31930′ code (the main roads). The region enclosed by roads tends to have relatively homogeneous socioeconomic functions [31] so they can serve as an optimal search domain for text similarity measurement. Second, the Jaro–Winkler metric [32] in the Python package (https://pypi.org/project/pyjarowinkler/) was utilized to measure the similarity of POI names within one analysis unit. This metric considers the length of the common prefix of two texts and primarily for short texts [33]. Therefore, this metric is suitable to calculate the degree of similarity between two POI names (i.e., ‘Unclassified’ and ‘Reclassified’). Following Chen et al., [5], the similarity threshold of 0.85 was used. If the similarity value of two POI names is larger than 0.85, these two POI names were considered as ‘similar’ POIs. Third, we appended the corresponding building type of similar ‘Reclassified’ POI to the ‘Unclassified’ POI category.

3.1.2. Topic Modeling

The topic modeling was used to identify the corresponding building types of ‘Unclassified’ POIs without similar POIs within an analysis unit in four steps. We took the name of each POI as a sentence, the collection of POIs with the same category as a document, and the category as a topic. First, we used Jieba (https://github.com/fxsjy/jieba), a Python library for segmenting words in Chinese, to conduct words segmentation for each ‘Reclassified’ POI name. Each collection of words consists of words segmented from each reclassified POI category. Second, we used the term frequency-inverse document frequency (TF-IDF) calculated by Equation (1) in the in-house Python code to measure how relevant a word is to a POI reclassified category. Considering that place names (i.e., streets, districts, cities, or counties) and non-Chinese words in POI names cannot be used as theme words of the reclassified POI category, we removed them before calculating TF-IDF. Third, we generated sets of theme words for each reclassified POI category via sorting TF-IDF values. The larger the TF-IDF value of a word, the more important this word was to distinguish its corresponding building types. We sorted the TF-IDF value of words from high to low and extracted words with the TF-IDF value larger than turning points as theme words (Figure 7). Here, we explained theme words selection by taking hospital as an example. The first three words in the rank of hospital TF-IDF values were ‘Hospital’ (0.029),’ Stomatology department’ (0.023), and ‘Outpatient service (0.020) and they were used as theme words to identify hospital POIs from ‘Unclassified’ POIs. The TF-IDF values of ‘Community’ (6.03 × 10⁻⁴), ‘Health’ (5.79 × 10⁻⁴), and ‘Medicine’ (8.4 × 10⁻⁵) were smaller than 8.5 × 10⁻⁴, which means these words are not important enough to identify hospital POIs from ‘Unclassified’ POIs. Finally, we conducted the matching of theme words (Equations (2) and (3)) to determine corresponding building types of ‘Unclassified’ POIs.

$$TF-ID{F}_{ti}=\frac{T_i}{A_i}\times \log \left(\frac{N_i}{d{f}_t}+1\right)$$

(1)

where $TF-ID{F}_{ti}$ is the TF-IDF value of the word t in the reclassified POI category i; $T_i$ is the number of the word t in the reclassified POI category i; $A_i$ is the total number of words in the reclassified POI category i; $N_i$ is the total number of reclassified POI categories ($N_i=7$ in this study); and $d{f}_t$ is the number of reclassified POI categories with the word t.

$$w_j=\min \left(N_{w\in theme}\right)$$

(2)

$${\mathrm{POI}\mathrm{categoty}}_i=\{\begin{array}{c} Category | w_j \left(N_{w_j}=1\right)\\ Cateogry |\min \left(\frac{Ran{k}_{w_j\in theme}}{T{N}_{w_j\in theme}}\right) (N_{w_j}>1)\end{array}$$

(3)

where the ${\mathrm{POI}\mathrm{categoty}}_i$ is the corresponding building type of ‘Unclassified’ POI i; $N_{w\in theme}$ is the number of sets of theme words with the word w; $w_j$ is the word with the minimum $N_{w\in theme}$; $N_{w_j}$ is the number of $w_j$; $Ran{k}_{w_j\in theme}$ is the TF-IDF value rank of $w_j$ in the corresponding set of theme words; and $T{N}_{w_j\in theme}$ is the total number of words in the corresponding set of theme words.

3.2. Identification of Building Types

We identified building types based on land use parcels and reclassified POIs. First, we interpreted code of land use parcels based on the Google Earth images and online Baidu Maps (

Table 2. The codes and corresponding building types of land use parcels.

Code	Land Use Type	Building Type
3192	Residential districts	Residence
3197	Government, Industrial district, Company	Office
3194	Hospital	Hospital
3195	Primary/Secondary/High School, Kindergarten	School
5640	University	School
3193	Park	Office
36126/36130	Shopping mall	Retail
3201	Resort Hotel	Hotel
3198	Airport	Office
4125/5644	Parking lots	Unrelated
31642/41124/41150/4128-4144/41472/6301	Subway station	Unrelated
3185/5636-5638	Lake/waterway	Unrelated
5645-5650/3177/3182	Sport ground	Unrelated
3174/5642/5643	Greenspace	Unrelated

). After performing overlay analysis, we appended land use types to building footprints within land use parcels to obtain building types. Second, for building footprints outside land use parcels, considering the uneven distribution of POI data shown in Figure 8A, we calculated the indicator of type ratio for each building with POIs nearby and the indicator of area ratio for each building without POIs nearby to obtain building types.

3.2.1. Type Ratio

For buildings with POIs nearby, type ratio (TR) was used to determine the reclassified POI category that can be appended to buildings. This ratio is calculated as the percentage of the reclassified category of POIs among the total number of POIs in the building’s buffer region (Equation (4)). Due to the geographical deviation between POIs and building footprints, not all POIs are exactly located within building footprints. Therefore, we generated the buffer for each building. The radius of the building’s buffer is the half of the Euclidean distance between this building and its nearest building because a POI is not likely to be shared by two buildings. In a mixed-use high-rise building (Figure 8), restaurants, retails, and offices dominate the bottom floors and residence dominates the remaining upper floors. The building type was determined by the major types (i.e., residence, school, and hospital) that dominate most floors. TR was calculated for POIs with the major types in each building buffer. The building type with the maximum type ratio was appended to the building footprint (Figure 9).

$$T{R}_i=\frac{n_i}{N_j}\times 100\%$$

(4)

where $n_i$ is the number of the reclassified POI category $i$; and $N_j$ is the total number of POIs in the building’s buffer region $j.$

3.2.2. Area Ratio

For buildings without POIs nearby, area ratio (AR) was used to determine the type of neighboring buildings that can be appended to these buildings. This ratio is calculated as the percentage of the building footprint that intersects with the Thiessen polygon of its neighboring buildings (Equation (5)). The regions with the sparse POI data shown in Figure 8 are distributed in urban fringe districts and mainly include old-fashioned dwellings and warehouses. Compared to the highly heterogeneous building types in urban core areas, building types in urban fringe districts are more homogenous. Therefore, in the regions with sparse POI data, the closer two buildings are to each other, the more similar building types of them are. We generated Thiessen polygons using the centroid of building footprints whose types have been identified by the indicator of TR, and appended building types to the corresponding Thiessen polygons. The larger a building area was covered by the Thiessen polygon, the larger this building type was influenced by this Thiessen polygon. Thus, the type of Thiessen polygon with the maximum area ratio was appended to the building footprint (Figure 9).

$$A{R}_i=\frac{A_{Thiessen polygon\cap building}^i}{A_{building}}\times 100\%$$

(5)

where $A_{building}$ is the total area of the building footprint; and $A_{Thiessen polygon\cap building}^i$ is the area of building footprint covered by Thiessen polygon with type $i$.

3.3. Accuracy Assessment

We evaluated the reclassified ‘Unclassified’ POIs and identified building types using the stratified random sampling method. First, a total of 500 ‘Unclassified’ POIs were used to assess the performance of NLP-based approach. The numbers of sample POIs for seven building types (i.e., Hospital, Hotel, Office, Residence, Restaurant, Retail, and School) are 20, 25, 95, 270, 20, 40, and 30, respectively. Second, a total of 500 building footprints were used to assess the performance of two building identification indicators. The numbers of sample buildings for seven building types are 20, 25, 165, 150, 35, 60, and 45, respectively. These sample buildings were manually interpreted based on the street view and online map of Baidu (https://map.baidu.com/). Confusion matrices, overall accuracy, producer’s accuracy (PA), user’s accuracy (UA), and kappa coefficient were calculated in the accuracy assessment.

4. Results

4.1. Spatial Pattern of Identified Building Types

Seven building types show significantly different patterns across the study area (Figure 10). Office and residence buildings accounted for the largest percentage of the total buildings. Compared to office buildings, residence buildings were mostly in clusters. Restaurant, retail, and hotel buildings were mainly inside the 2nd ring roads. School buildings were mainly clustered in the northwest corner of the region between the 2nd and 5th ring road. In comparison, hospital buildings were more homogeneously distributed in this study area. The patterns of retail and restaurant buildings are very similar. In terms of size, restaurant and retail buildings tended to be smaller than school and office buildings.

4.2. Performance of the NLP-Based Approach

The proposed NLP-based approach performed well in reclassifying ‘Unclassified’ POIs. For most ‘Unclassified’ POIs (83.0%), similar names were found in ‘Reclassified’ POIs, which proved the universality of the web mapping service rule we proposed in Section 3.1.1. The 15.3% of ‘Unclassified’ POIs were reclassified using topic modeling. The type of remaining 1.7% of ‘Unclassified’ POIs was reclassified as residence. The overall accuracy was 89.0%, with a kappa coefficient of 0.83 (

Table 3. The confusion matrix of POI reclassification via natural language processing (NLP) techniques.

	Hospital	Hotel	Office	Residence	Restaurant	Retail	School	Total	UA (%)
Hospital	20	0	0	0	0	0	0	20	100.00
Hotel	0	15	6	4	0	0	0	25	60.00
Office	1	1	70	19	0	1	3	95	73.68
Residence	0	0	2	266	0	1	1	270	98.52
Restaurant	0	1	0	6	12	1	0	20	60.00
Retail	1	0	4	2	0	33	0	40	82.50
School	0	0	0	1	0	0	29	30	96.67
Total	22	17	82	298	12	36	33	500
PA (%)	90.91	88.24	85.37	89.26	100.00	91.67	87.88

). Specifically, identified hospital, residence, and school POIs showed the highest accuracy, and their producer accuracy (PA) and user accuracy (UA) were all around 90%. In general, hotel and restaurant POIs showed a low UA of 60% because they could easily be misclassified as office and residence POIs, respectively. Compared to PA, the UA of office POIs was lower, because office POIs could be easily misclassified as residence POIs.

4.3. Accuracy Assessment of Identified Building Types

The accuracy assessment indicates that our building type identification methods performed well in identifying building types. The percentage of buildings identified based on land use parcels was close to that based on the method of the area ratio (Figure 11). The overall accuracy was 78.2% with a kappa coefficient of 0.71 (

Table 4. The confusion matrix of building type identification.

	Hospital	Hotel	Office	Residence	Restaurant	Retail	School	Total	UA (%)
Hospital	16	0	1	2	0	1	0	20	80.00
Hotel	0	11	5	9	0	0	0	25	44.00
Office	0	0	114	38	1	9	3	165	69.09
Residence	0	0	2	145	0	3	0	150	96.67
Restaurant	0	0	4	8	23	0	0	35	65.71
Retail	0	0	7	12	0	41	0	60	68.33
School	0	0	1	3	0	0	41	45	91.11
Total	16	11	134	217	24	54	44	500
PA (%)	100.00	100.00	85.07	66.82	95.83	75.93	93.18

). The high UA (80%, 91.1%) and PA (100%, 93.2%) of hospital and school buildings showed that our methods could identify hospital and school buildings well. The high UA (96.7%) and low PA (66.8%) of residence buildings indicated their high omittance. The omitted residence buildings were mostly misclassified as office buildings, leading to a low UA (69.1%) of office buildings. Moreover, the high PA (>95%) of hotel and restaurant buildings indicate that they were not easy to be omitted, but they could be misclassified as residence and office buildings. The UA and PA of retail buildings were both low, indicating that this building type could not be identified well.

5. Discussion

5.1. Comparisons with Other Methods

Our framework shows a good performance for identifying over 440,000 classified building footprints, especially for hospitals and schools. We summarized recent studies related to building type mapping in

Table 5. The list of case studies related to building type identification.

Case	Category	Study Area/The Number of Buildings	Data Source	Characteristics	Method	Building Types	Accuracy
[1]	RS-based 1	Denver, USA/1510 buildings	LiDAR	Geometry, landscape	Supervised machine learning approaches (SVM, Random Forest, etc.)	Single-family houses, multiple-family houses, non-residential buildings	OA2 > 70%, Kappa8 > 0.5
[10]	RS-based	Yangon, Myanmar/64.11 km2	GeoEye/Landsat/NPP-VIIRS	Geometry, Spectrum	Unsupervised machine leaning (Hierarchy classification)	Residential, commercial, industrial buildings	OA = 76% Kappa = 0.58
[7]	RS-based	A small area of Beijing, China	Quickbird/Worldview	Geometry, texture	Supervised machine learning (Back Propagation Neural Network Algorithm)	High-rise buildings, multi-story residential buildings, old-fashioned courtyard dwellings	OA = 91.5% Kappa = 0.892
[8]	RS-based	A small area of Beijing, China/8831 buildings	Quickbird	Geometry, texture, spectrum	Supervised machine learning (Random Forest)	Low-story shantytowns, medium-story apartments, high-rising apartments, administrative buildings, commercial buildings, industrial buildings, auxiliary buildings	OA = 79.54% Kappa = 0.72
[9]	RS-based	Cologne, Dresden, German	IKONOS, Airborne laser scanning data	Geometry	Unsupervised machine leaning (Fuzzy logic classification)	Non-residential/industrial, detached/semi-detached, terraced, building blocks and high-rise buildings	NA
[34]	Map-based	Dresden, Halle Krefeld, Stolpen, Saxony, German	Building footprint vector data, topographic raster maps	Geometry	Supervised machine learning approaches (SVM, Random Forest, etc.)	Single/two-family houses, multi-family houses, industrial/commercial, special purpose	OA is about 90%
[15]	Map-based	City of Zurich, Switzerland	Building footprints vector data from topographic map and MasterMap	Geometry	Supervised machine learning approaches (SVM, AdaBoost, etc.)	Industrial and commercial areas, inner city, dense buildings, disperse buildings, single building	OA = 75% Kappa = 0.66
[12]	SS-based	Haizhu District, Guangzhou, China/20,928 buildings	Taxi GPS trajectory data/Tencent user density data	Spatiotemporal distribution characteristics	Unsupervised machine leaning (K-means clustering)	Public facilities, multistore Residential buildings, high-rise residential buildings, business and service buildings, urban village	OA = 85.66% Kappa = 0.8174
[13]	SS-based	Tianhe District, Guangzhou, China 68,997 buildings	Taxi GPS trajectory data/Tencent user density data/POI database	Spatiotemporal distribution characteristics	Unsupervised machine leaning (DBSCAN clustering)	Residential buildings, offices, shopping centers, hotels, hospitals, schools	OA = 72.22%
[14]	SS-based	Tianhe District, Guangzhou, China/63,961 buildings	Taxi GPS trajectory data/Tencent user density data/POI database	Spatiotemporal distribution characteristics	Probabilistic model	Single function building, Multifunctional building (Recreation, office and residential building; Recreation and residential building, etc.)	OA = 85%
[16]	RS/SS-based	Bangkok, Thailand/2 km2	ALOS/POI database	Spectrum, geometry, land use	GIS spatial analysis functions and logical statements (if–then–else)	Residential (single house, townhouse), Commercial (single, townhouse), Industrial (factory, warehouse), Theater, Shopping mall	OA > 75%

¹ RS-based: remote sensing-based building type identification studies. OA: Overall Accuracy, Kappa: Kappa Coefficient. SS-based: social sensing-based building type identification studies.

. Compared to remote sensing-based studies and map-based studies, our framework can identify more non-residential building types (e.g., school, hospital, hotel, restaurant and retail) with a comparable overall accuracy of 78.2% and kappa coefficient of 0.71. To be specific, previous studies typically required very high resolution (VHR) images (e.g., Quickbird and Worldview) to extract detailed physical characteristics (e.g., spectrum, texture, and geometry) of buildings or utilized building footprints data from topographic maps to extract geometry characteristics of buildings directly. The limitations of these studies exist in two aspects. First, the high cost of data source limited their applications over large areas. Second, although these studies utilized supervised or unsupervised machine learning approaches to fuse characteristics extracted from multiple data sources, socioeconomic characteristics related to human activities [11] were not considered in these characteristics. As a result, their building type maps mainly included residential and non-residential types because detailed buildings types (e.g., hospital and school) had similar physical characteristics and needed additional socioeconomic characteristics to be differentiated. For example, when the remote sensing-based study [16] included POI data, they identified two additional non-residential types (i.e., theater and shopping mall) because they used additional socioeconomic features (i.e., land use) from POI data. Compared to the social sensing-based methods, our framework is more transferrable to other China cities and has lower uncertainties in terms of identifying detailed building types. Due to the limitations of data availability, existing social sensing-based datasets (e.g., mobile phone data and taxi trajectory data) are mostly accessible in big cities, but the POI data used in our framework are available even in small cities [21]. Moreover, POI data can be used to infer building types more directly compared to social sensing data because they have a specific category attribute that can be linked with human activities. Social sensing-based methods identified building types via unsupervised clustering temporal human activity curves, the low representativeness and density of social sensing data had influence on the detection rate of building types such as schools, hotels, and hospitals. For example, hospital and school had low use of Tecent software [13] or educational trips were seldom shown in taxi trajectories [12], leading to the detection rate of school and hospital buildings being low. In contrast, our framework can identify hospital and school buildings well with a high UA (80%, 91.1%) and PA (100%, 93.2%).

5.2. Implications

The framework and product from this study have important implications for urban planning and management. The automatic approach proposed in our framework can maximize the usage of POI data in urban studies. When the category attribute of POIs is not enough to infer human activities, the name attribute is used to derive human activity information via the NLP-based approaches. As shown in Figure 4c,d, we used NLP-based approaches to classify ‘Unclassified’ POIs so that POIs can be used to infer the types of surrounding buildings. Moreover, the product of building type is one of the required inputs in urban studies at fine spatial scales. For example, Li, et al. [3] utilized the city-wide building type information obtained from the assessor’s database to estimate the building energy use at the building level. In other countries such as China, due to the lack of building information, the building energy use studies mainly used macro statics data over large scale [35,36] instead of using quantitative modeling at fine spatial scales. In addition, our product of building type provides the possibility to generate spatial datasets related to human activities. For example, human activities influence waste generation patterns, disaster loss amount, or population density of surrounding buildings. The information of building type can serve as an indicator to estimate waste construction [4], analyze damage and vulnerability [5], and map population [6] at the city scale.

5.3. Future Work

Further research can be focused on supervised learning algorithms to reclassify ‘Unclassified’ POIs. The topic modeling proposed in this study is a simple algorithm for matching words segmented from each POI name to theme words of the POI categories. In terms of reclassification of restaurant and hotel POIs, this algorithm performs not well with a UA of 60%. The classifier of supervised learning algorithms can be trained with TF-IDF values of words segmented from different reclassified POI categories, to automatically assign the most relevant reclassified POI category to each ‘Unclassified’ POI. Therefore, the TF-IDF extraction method coupled with a supervised learning algorithm could improve the performance of reclassification of ‘Unclassified’ POIs.

In addition, a further study is needed to improve the accuracy of identifying building types in regions with sparse POI data. The types of buildings in historical residential regions cannot be identified accurately because POIs of these buildings are not collected by the web mapping platform. Some historical residence buildings are misclassified into hotel, restaurant, retail or office buildings, leading to a low PA of residence buildings (66.8%) and low UAs of hotel (44.0%), restaurant (65.7%), retail (68.3%), and office (69.09%) buildings. Buildings of these types are sparsely scattered in the historical residential regions and their impacts on neighboring residence buildings can be considered in future studies. Moreover, the size of these buildings can be considered in the future improvement to mitigate their impacts because these small buildings are often used to provide service for residents.

6. Conclusions

Building type is of great use for analyzing urban socioeconomic features dominated by human activities. The products of building types can help researchers in studying human activities (e.g., population density mapping, building energy use modeling) at fine spatial scales. In this study, we proposed a new framework to identify building types in the urban region. First, we employed NLP-based approaches (i.e., text similarity measurement and topic modeling) to extract semantic information hidden in POI names. The derived information was used to better infer surrounding building types. Second, we proposed ratio-based approaches (i.e., type ratio and area ratio) to identify building types. The proposed framework is capable of identifying building types over large areas (e.g., the region within the 6th Ring Road of Beijing, China). The accuracy assessment indicates that our framework is reliable to reclassify POI categories with an overall accuracy of 89.0% and kappa coefficient of 0.83, and to identify building types with an overall accuracy of 78.2% and kappa coefficient of 0.71. NLP-based approaches can maximize the usage of POI data in identifying building types. The resulting product of building types is of great use for urban planning and management because building type information is one of the required inputs in quantitative urban studies. Future studies are needed to improve the performance of identifying building types in regions with sparse POI data and can be focused on potential techniques (e.g., supervised learning algorithm) to reclassify ‘Unclassified’ POIs and transferability of the approach in other countries.