Satellite mapping of every building’s function in urban China reveals deep built environment disparities

Domain: chemistry
Published: 2026-02-16
Editorial outcome: minor_revision

Abstract
Decades of rapid urbanization have reshaped China’s cities, yet fine-scale built environment disparities remain unclear due to scarce building-level data. Here, we present SinoBF-1, a national building functional map of China that delineates 110 million buildings across 109 major cities using 1-meter multi-modal satellite data. Using nine indicators spanning urbanization intensity, facility accessibility, and infrastructure sufficiency, we quantify disparities across city tiers, geographic regions, and intra-city zones. Analyses reveal that: (1) Across city tiers, accessibility and amenity diversity decline sharply from top- to low-tier cities, while mid tiers show more equitable housing allocation; (2) Geographically, southern cities exhibit the highest access to healthcare, education, and public services but suffer from infrastructure overcrowding; and (3) Within cities, later-expanding zones exhibit greater disparities than early-established urban cores. This study reflects legacies of national development policies over the past half-century and offers a framework for evaluating urban inequality in rapidly urbanizing regions.

Introduction
Cities—home to more than half of the global population—serve as major engines of socio-economic development and generate 80% of the world’s gross domestic product (GDP)1. Nowhere is this urban dynamism more evident than in China, where four decades of rapid urbanization, driven by reform and opening-up policies, have transformed the nation into a global economic powerhouse, contributing one-fifth of the world’s GDP and hosting 66% of its population in cities2. Yet beneath this growth lies a deep and persistent spatial disparity3. Despite massive investments, industrialization, and modernization, cities across China exhibit striking disparities in livability, access to infrastructure, and economic opportunity4. Although administrative city tiers are not standardized metrics of urban form or function, they serve as a practical proxy for China’s urban hierarchy, where higher-tier cities have historically received greater policy support and infrastructure investment than lower-tier counterparts5. Geographically, the 1935-proposed “Hu Huanyong Line” divided China into two contrasting parts, where the southeastern part with 43% of China’s total area carries 94% Chinese population and produces 95.7% of China’s GDP6. National policies, such as the “Northeast Industrial Revitalization Plan”7, were introduced to mitigate enduring scars of inequalities brought by historical legacies of war preparedness and industrial relocation8. These geographic and socio-economic disparities undoubtedly challenge equal residential environments across China. The inequalities that manifested in resources9, facilities10, housing11, etc., threaten long-term socio-economic advancement and harm sustainable development12,13. However, efforts to monitor and understand urban disparities have been hampered by the absence of fine-scale, nationwide data and by the lack of comprehensive frameworks capable of capturing the multi-dimensional nature of disparities. Here, we overcome both limitations and present a national-scale building functional map of China (SinoBF-1), covering 110 million buildings. Our results revealed a systematic, multi-dimensional disparity spanning urbanization intensity, facility accessibility, and infrastructure sufficiency. Our analysis provides a valuable framework that situates today’s patterns in their policy legacies, contributing to the ongoing discourse on inequality in the built environment.

As the most granular units of urban form, buildings are the foundational elements of both residential life and economic activity. Their spatial distribution and functional composition directly shape the fabric of cities14. Understanding the attributes of individual buildings is therefore essential for capturing the fine-scale structure of urban environments and the inequalities embedded within them15. Advances in high-resolution satellite imagery now offer an opportunity to observe cities at the scale of individual buildings16,17. To date, most building-level studies have focused on geometric attributes, such as footprint, height, and density, to evaluate their effects on transportation systems18, public health19, and energy use20. Many studies of building footprints or rooftop mapping have achieved significant success at a global scale21,22. However, while we now know where buildings are and how they are distributed, we still know remarkably little about what individual buildings are used for. This functional blind spot represents a critical limitation in current urban research. Recent effort has developed a dataset that provides functions for 31 million buildings23 in China, yet research in this field remains at its early stages, severely constraining our ability to analyze national-scaled disparities using fine-grained building functions and resource distribution.

Numerous studies have sought to assess disparities in the built environments, yet most have been constrained by coarse spatial resolution and a narrow, single-dimensional focus. For instance, global-scale travel-time maps at a 1-km resolution have been used to measure accessibility as a proxy for resource disparities24. Similarly, unequal allocation in infrastructure25, public services26, green spaces27, and household water28 have been evaluated using low-resolution grids (≥200 m) or aggregated statistical data—each study addressing isolated dimensions in fragmented ways. These unidimensional approaches, though insightful, fall short in capturing the complexity of spatial disparities, which are inherently multifaceted and spatially heterogeneous. As a result, current knowledge is limited in both resolution and scope, leaving many critical indicators and their interactions underexplored. Addressing this gap requires a paradigm shift toward high-resolution and multi-dimensional assessments that can reveal the full extent and structure of urban disparity.

A multi-level system is also needed to understand how spatial disparities unfold within China’s unique socio-institutional context. First, the city tiers reflect China’s administrative and economic hierarchy, shaping how resources are distributed among cities29. Second, geographic regions capture historical and environmental divides—the coastal-inland gradient—that continue to structure disparities in development and population4. Finally, intra-city zones reveal differences between early-established urban cores and later-expanding urban zones, where residents directly experience unequal access to infrastructure and services.

Here, we present SinoBF-130, a national-scale building functional map of China derived from more than 69 terabytes of multi-source satellite data, including optical imagery, nighttime lights, and altimetry observations. This fine-grained dataset captures the attributes and functions of approximately 110 million buildings across 109 cities, covering a total area of 1,094,344 km2. To systematically assess residential inequality and its drivers, we introduce a multi-dimensional framework31 comprising nine fine-scale indicators that are closely associated with the built environment and residents’ daily lives (see Supplementary Table 1). They are grouped into three core dimensions: urbanization intensity, facility accessibility, and infrastructure sufficiency. Each assessment dimension is explained as follows: (1) Urbanization intensity is represented by building height, density, and nighttime light intensity; (2) Facility accessibility is quantified by the travel time from each residential building to the nearest education, healthcare, and public service facilities, reflecting residents’ access to essential urban services; (3) Infrastructure sufficiency captures the diversity and adequacy of local urban form, using metrics of neighborhood amenity diversity, housing inequality, and per capita infrastructure occupation. We then address how institutional, geographical, and local mechanisms shape patterns of disparities across the urban landscape by answering two questions: (1) What are the national patterns of building function across China’s urban landscape? (2) How do disparities in the residential built environment vary across city tiers, geographic regions, and intra-city zones? By addressing them, this study provides a lens to understand the structure of disparities in rapidly urbanizing contexts. This approach establishes a scalable way for assessing global urban development’s spatial inequality, helping design more equitable, inclusive, and sustainable cities.

Results
Overview of building function distributions in urban China
To classify the functions of buildings, we define eight building categories according to the construction classification standards of China. These include residential, commercial, administrative, healthcare, sport and art, educational, industrial, and public service. Representative examples and visual legends are presented in Fig. 1a. According to China’s city hierarchical system, there are five city tiers that reflect decreasing gradients of political influence, economic activity, industrial capacity, and population size from Tier 1 to Tier 529. We selected 109 cities that cover all city tiers and geographical regions, accounting for about half of China’s population and 70% of its GDP (Fig. 1b, Supplementary Fig. 1). To assess the reliability of our building-level functional classification, we conducted validations at the statistical, building, and point levels using two independent sources: (1) official government reports from the National Bureau of Statistics32 and the Ministry of Housing and Urban-Rural Development of China33; (2) large geospatial reference point sets comprising 5,280,695 field observation samples from Amap Inc. As shown in Supplementary Fig. 2a and Supplementary Table 2, the resulting functional map achieved strong agreement with external benchmarks, with an average R2 of 0.8208 at the statistical level and an overall accuracy of 84.65% at the individual building level (see “Methods” for details).

Analysis of the national building function (Fig. 1c, Supplementary Fig. 2b) reveals a clear alignment between urban functional structure and economic specialization of cities in GDP compositions across tiers34. Higher-tier cities, particularly Tier 1 and Tier 2, are characterized by a greater prevalence of commercial buildings, reflecting the dominance of service-based economies. For example, Macau, a Tier 2 city in southern China, exhibits the highest proportion of commercial buildings among all cities analyzed (36.81%), reflecting its exceptional service-oriented economy in which the tertiary sector accounted for 92.26% of GDP in 2021. In contrast, Tier 5 cities in the southwest show a predominance of industrial buildings, indicating their manufacturing- and resource-based economic structures. Panzhihua, a major hub for steel, vanadium, titanium, and energy production, recorded the highest share of industrial buildings (54.1%), with the secondary sector comprising 54.8% of its GDP.

We assess the residential built environment across 109 cities using nine building-level indicators grouped into three key dimensions: urbanization intensity, facility accessibility, and infrastructure sufficiency (Fig. 2). Together, these indicators enable a multi-dimensional evaluation of spatial disparities at a fine scale. In the following sections, we apply this framework to examine how disparities manifest across China’s urban hierarchy—first among city tiers, then across broad geographic regions, and finally within individual cities.

Urban disparities along China’s city hierarchy
Urbanization intensity (Fig. 2a), as captured by building height, density, and nighttime light intensity, varies systematically across China’s city tiers, yet reveals notable and unexpected patterns (Fig. 3a). The distribution of building height in Tier 2 cities shows a surprising deviation: the mode value is the lowest among all tiers (10 m). By contrast, Tier 2 cities exhibit the highest average building density across all tiers, where the representative city, Zhengzhou (Fig. 1b), stands out as the most spatially compact city, with a density exceeding 400 buildings per km2. This suggests that Tier 2 cities, despite their economic significance, may favor horizontally expansive development over vertical growth. These patterns indicate that density and height—though often correlated—may follow divergent trajectories depending on development strategy and planning constraints at the tier level. Overall, the results show that Tier 1 cities have the tallest buildings, but the differences among tiers remain relatively modest, ranging from 13 to 10 m. In the building density, Tier 2 cities exhibit a slightly higher density than Tier 1, while both are substantially higher than the other tiers.

Facility accessibility was evaluated using the high-resolution map that integrates our building functions with a hierarchical traffic network and 1-m land-cover product that we previously produced (Supplementary Fig. 3). This map quantifies the minimum travel time required for each residential building to reach its nearest education, healthcare, and public services (Figs. 2b and 3b). Tier 1 cities exhibit markedly higher accessibility. Over 78% of residential buildings in Tier 1 cities are within a 5-min walk of the nearest education facilities, compared to 22% in Tier 5 cities. Similar disparities are observed for healthcare (52 vs. 28%) and public service (47 vs. 16%). In Tier 5 cities, more than 70% of residents must travel over 15 min to access at least one of these essential facilities. Furthermore, similar accessibility patterns were observed using either population or residential buildings on the y-axis, with Tier 1 and 2 residents reaching facilities substantially faster than those in lower-tier cities. The accessibility maps across five representative cities (Fig. 3c, Supplementary Fig. 4) further reveal a consistent pattern: areas with high population density tend to coincide with zones of high facility accessibility.

Infrastructure sufficiency, the third dimension to evaluate spatial disparities in cities, reveals substantial variations across city tiers (Fig. 4). The first indicator, neighborhood amenity diversity, captures both the richness and availability of six facility types (e.g., commercial, sport and art, etc.) within 15-min walking zones. Tier 1 and 2 cities cluster in the high-diversity, high-availability quadrant of Fig. 4a. In contrast, the majority of Tier 4 and 5 cities fall in the low-diversity, low-availability quadrant, reflecting more limited neighborhood amenity diversity. The second indicator, housing inequality, is quantified using the Gini coefficient (see “Methods” for details), offering a proxy for equity in housing volume across the population (Fig. 4b). Tier 3 cities exhibit the lowest median disparity (0.56), outperforming Tier 1 and 2 cities. These results suggest that moderately developed cities may offer more equitable living conditions, despite having fewer total resources. The third indicator measures per capita occupation of healthcare and public service, reflecting the adequacy of infrastructure supply relative to local demand (Fig. 4c, d). Metropolises like Shanghai report the highest per capita provision overall. Conversely, major Tier 1 cities such as Shenzhen and Guangzhou exhibit lower-than-expected healthcare space per person, trailing behind many Tier 3 and 4 cities. Together, these findings challenge the assumption that infrastructure sufficiency scales directly with economic development. While metropolises are often presumed to offer more equitable amenities, our analysis reveals that mid-tier cities can outperform higher-tier cities. These patterns highlight the potential advantages of moderately developed urban systems.

Uneven development across China’s geographic regions
China’s residential built environments also exhibit deep regional disparities, shaped by long-standing imbalances in population distribution, economic development, and infrastructure investment3. In terms of facility accessibility (Fig. 2b, Supplementary Fig. 2c), the southern and eastern regions consistently demonstrate the highest accessibility to educational, healthcare, and public services, reflecting more integrated urban facilities. Central and northern regions show particular strength in educational accessibility, supported by resource-rich cities like Wuhan and Beijing35. In contrast, the northwestern regions face substantial deficits in facility access, especially for healthcare and public services.

Regional patterns of infrastructure sufficiency echo these divides (Fig. 2c). The eastern region reports the highest neighborhood amenity diversity, with a Shannon entropy index of 0.76 (Fig. 4a, Supplementary Fig. 5c), while the northwest lags far behind at 0.27, indicating a more monotonous and insufficient facility landscape. Housing inequality also varies markedly: the northern and northeastern regions show the greatest disparities in residential capacity allocation, despite having the highest proportion of residential buildings (84.06 and 83.97%, respectively). Cities like Hohhot and Anshan exhibit the most unequal distributions, with Gini coefficients of 0.77 and 0.74, respectively. By contrast, the southern region demonstrates the most equitable housing distribution, with an average inequality index of 0.54. However, equity in housing is not necessarily related to adequate infrastructure provision. Southern cities generally face more overcrowded public infrastructure, with per capita space for healthcare and public services averaging just 0.006 and 0.031 m2, respectively—substantially lower than values in central, eastern, and northern regions (Fig. 4c, d). Northwestern cities, meanwhile, confront compounded challenges of both limited facility access and low functional diversity (Fig. 2c), reinforcing spatial disadvantage across multiple dimensions.

Built environment disparities within cities
To evaluate spatial disparities within cities, we further divided each studied city into three zones—urban core, intermediate zone, and urban periphery—based on their development stage, functional planning, and administrative boundaries. This zoning approach enables a consistent comparison of infrastructure and service disparities across urban gradients. As a typical sample illustrated in Fig. 5a, the delineation of zones in Beijing shows distinct population concentrations. In the assessment, we focused on facility accessibility (Fig. 5b) and infrastructure sufficiency (Fig. 5c) because the first dimension (i.e., urbanization intensity) is inherently defined at the city-wide scale rather than intra-urban zones.

In terms of facility accessibility, urban cores consistently offer the shortest travel time to essential facilities. On average, residents in urban cores reach education, healthcare, and public service facilities within 15, 9, and 18 min, respectively. In contrast, those in intermediate zones face substantially longer travel time—43, 24, and 51 min—while residents in urban peripheries endure the heaviest travel burdens, requiring 69, 37, and 83 min, respectively, to access these functional buildings. These patterns reflect a classic core-periphery structure, consistent with global urban trends26, and highlight the persistent service accessibility challenges faced by populations in expanding peripheral zones.

With regard to infrastructure sufficiency, our analysis challenges the common assumption that intermediate zones represent sites of future urban advantage36. Instead, we find that these zones exhibit the highest disparities in residential capacity allocation, with a Gini coefficient of 0.56—compared to 0.41 in urban cores and 0.54 in urban peripheries. Meanwhile, peripheries that typically have lower population densities report the highest per capita public infrastructure provision (0.18 m2), exceeding both urban cores (0.16 m2) and intermediate zones (0.10 m2). This suggests that while peripheral areas may benefit from lower demand, intermediate zones are marked by both high population pressure and insufficient infrastructure expansion. To further compare the effects of different intra-city regional division methods on the results, we attempted to delineate these areas based on population and building density, and the results showed strong consistency. The overall assessments of infrastructure sufficiency indicate that urban cores continue to offer the most well-resourced residential environments, largely due to their early development and historic concentration of facilities37. Despite targeted planning initiatives aimed at rapidly developing intermediate zones38, these areas still suffer from overcrowded infrastructure (low per capita allocation) and housing problems (high inequality index).

Methods
The overall workflow of this study encompasses two key parts: (1) national-scale building function mapping and validation; (2) multi-level assessments of residential built environments. This section comprehensively explains the data sources, the proposed mapping models, and the assessment indicators. In Supplementary Figs. 6 and 7, we demonstrate the details of the workflow and main operations. By integrating these elements, we aim to provide an overall workflow for building-scale mapping, validation, and residential built environment assessment.

Data
To comprehensively characterize building attributes and identify their functions, we utilized three modalities of satellite data as mapping sources, where Supplementary Fig. 8 illustrates the typical samples of each data. First, to capture fine-scale building appearances, we acquired high-resolution (1.07 m/pixel) optical images from Google Earth imagery. By integrating imagery from advanced sensors and satellites, Google Earth imagery emerges as a freely accessible high-resolution data source with complete coverage of China. The cloud-free and high-quality imagery, produced through advanced pre-processing by Google Earth, makes it a suitable source for fine-scale building observation50. Second, to describe the stereoscopic attributes of buildings, we used the 10-m resolution building height data of China (CNBH-10m)51. The CNBH-10m, as the finest available building height dataset covering China, is generated through data fusion from Sentinel-1 and -2, PALSAR, LuoJia 1-01 satellites, and settlement footprint data, providing critical information on building verticality. Third, to assess nighttime production activities and potential energy consumption patterns, we incorporated 10-m resolution nighttime light images from the SGDSAT-1 satellite platform. As an advanced scientific satellite dedicated to SDGs52, SGDSAT-1 was launched in 2021 and equipped with a glimmer imager, a multispectral imager, and a thermal infrared spectrometer. This dataset offers a perspective on urban activity levels and energy use at a high spatial resolution, further enriching our understanding of building functionality.

To make better use of these data, we performed preprocessing on the input data to mitigate the potential impact of imaging noise on building function mapping. The procedure is illustrated in Supplementary Fig. 6. We first resampled the building height data and nighttime light data to 1.07 m per pixel based on the resolution of the optical imagery. Then, following previous processing workflows, we selected cloud-free or thin-cloud scenes from the SGDSAT-1 nighttime light imagery, and we performed stripe noise removal and radiance correction to mitigate the effects of imaging noise53,54. Furthermore, all three raster modalities were in the geographic coordinates system of WGS 1984. To ensure stricter spatial correspondence among them, we performed co-registration on the input raster data. Supplementary Fig. 8a–c shows the three processed input rasters. Finally, the processed multi-modal remote-sensing data were concatenated at the band level to form an aligned data cube for input into the deep learning model.

Besides, to assess the facility accessibility across 109 cities, the national-scale 1-m resolution land-cover map of China (SinoLC-1) that was produced in our previous study55 and hierarchical traffic networks from the OpenStreetMap were utilized as the auxiliary data. By combining these data sources with the produced building function map of this study, we were able to robustly calculate the travel time consumption between each residential building and other functional facilities to produce the 1-m-resolution, building-scale accessibility maps, ensuring a comprehensive and reliable analytical procedure.

Furthermore, to generate stable and reliable labels of building footprint and function information, we leveraged four types of open-access datasets. First, to define the precise boundaries of China’s building footprints, we utilized the CN-OpenData dataset56, which provides detailed building locations and boundaries for 90 major cities in China. To further supplement footprint annotations, we incorporated data from the East Asia Building Dataset57, which serves as an auxiliary source for enhancing annotation coverage across China. Second, for building function annotations, we compiled labeled samples by integrating land-use and area-of-interest (AOI) data from OpenStreetMap, a widely recognized Volunteered Geographic Information source. Although volunteer-contributed data may have classification or positional uncertainty, the accuracy and reliability of OpenStreetMap in China have been widely discussed and validated47–49. Many urban studies, both in China and globally, have used it as a critical data source for understanding urban patterns, applying it to discussions on urban sustainability, socioeconomics, transportation, and other aspects24,58,59. To accommodate the low noise-to-signal ratios in the OpenStreetMap labels, we further applied a weakly-supervised strategy in the model training protocol, screening for the reliable parts of the annotations. Together, these diverse data sources provided a comprehensive basis for accurate and detailed building function mapping at a national scale.

Lastly, to comprehensively evaluate the produced building function map, we incorporated a range of authoritative and high-quality datasets. Official government reports included the China Urban-Rural Construction Statistical Yearbook 2023 from the Ministry of Housing and Urban-Rural Development of China33 and the China Statistical Yearbook 2023 from the National Bureau of Statistics32, which were utilized as the statistical validation set. Furthermore, we employed 5,280,695 field-observed points of interest (POIs) from Amap Inc. to conduct comprehensive validation at the building level. These POIs were collected by field survey volunteers or building owners, encompassing more than 100 subcategories of real-life facilities (e.g., supermarket, coffee shop, museum, and clinic). To align with the eight building functionality types of this study, we further merged these subcategories following the classification guidelines provided by Amap Inc. Supplementary Tables 2 and 4 present the function definition, samples of these subcategories, and the class merging reference.

Deep learning-based mapping workflow
To accurately identify the 110 million buildings’ functions involved in 109 cities, we designed an efficient mapping framework referring to our previous studies on national-scale fine-grained mapping in China. Specifically, the workflow contains data preprocessing, GeoAI model training and inference, and post-processing.

As shown in the first step of Supplementary Fig. 6, the data preprocessing systematically integrates multi-modality satellite imagery and multi-source annotation data to construct robust training pairs for the deep learning model. Initially, we combined three spectral bands of high-resolution optical images, one building height band, and three bands of nighttime light data into a high-dimensional data cube. Prior to integration, the height and nighttime light bands were normalized to [0, 1] using min-max scaling, and the optical bands were standardized via z-score normalization to reduce illumination and seasonal effects. This fusion of data layers enables a comprehensive representation of each building. Subsequently, we unified over 100 categories of land-use and AOI data from OpenStreetMap into eight targeted building function categories, in alignment with China’s national standards, including the Standard of Construction Classification (No. GB/T 50841-2013), Unified Standard for Civil Building Design (No. GB 50352-2019), and General Specifications for Civil Buildings (No. GB 55031-2022). Next, the building footprints were overlaid with the unified function labels to generate the building-scale training labels. For efficient data organization, each city was divided into multiple storage tiles, each measuring 6000 × 6000 pixels. On average, approximately 30% of the buildings within each tile were annotated with their functions, ensuring sufficient labeled data for model training.

To efficiently exploit the generated training data for large-scale building function mapping, we employed the advanced deep learning model, Paraformer, which was specifically designed for large-scale map updating with limited labels in our previous studies60,61. Paraformer is a weakly supervised deep learning model that integrates a shallow convolutional neural network (CNN) branch with a vision transformer (ViT) branch. Supplementary Fig. 7a demonstrates the detailed structures of each branch. The CNN branch is constructed by five serially connected resolution-preserving (RP) blocks that were proposed in our previous study61. Each RP block contains parallel convolution layers with the sizes of 1 × 1, 3 × 3, and 5 × 5, whose steps are set to 1 for feature size maintenance. This structure allows the CNN branch to capture fine-grained building features. The ViT branch, which contains 12 transformer layers, leverages its global modeling capabilities to establish long-range dependencies among dispersed geographic areas. This dual-branch architecture enables the model to effectively handle the complex spatial heterogeneity of city environments. The semi-supervised training strategy further enhances the model’s ability to learn from the 30% reliable labeled samples. In detail, we modify the cross-entropy (CE) loss by considering the background and all function types as the supervised classes. Then, building objects without function-labeled information is regarded as unsupervised classes, which are ignored during loss calculation. Formally, for a training patch of the size of W × H, we used Y0, Yb, and G to represent the labels, results, and the unlabeled building mask, respectively. The modified CE loss can be written as:

L(Y0, Yb, G) = (Σ_{i=0}^{W} Σ_{j=0}^{H} Σ_{l=1}^{P} g_{ij} y0_{ij}(l) log(yb_{ij}(l))) / (MCE Sum(G_{ij}=1)), (1)

where y0_{ij}(l) and yb_{ij}(l) denote the pixel (i, j) of the label Y0 and prediction Yb with the class l. If the pixel is a building object without function-labeled information, the g_{ij} is set to 0; otherwise, the g_{ij} is set to 1. To account for regional variations in building attributes and urban layouts across China, seven separate models were trained for each geographic region, following the training approach adopted in our previous national-scale mapping study55. During training, each data tile—comprising an aligned data cube and labels with sizes of 6000 × 6000 pixels—was randomly cropped into 500 patches of 224 × 224 pixels. This approach maximized the use of training data while maintaining computational efficiency. A sample of the mapping result and the corresponding three-dimensional map are shown in Supplementary Fig. 8d, e. For model inference, a seamless mapping process was implemented. Input data cubes were assigned to their corresponding region-specific models, and a 112-pixel overlap was introduced between patches (224 × 224 pixels) to mitigate edge artifacts. The resulting patches were then merged and seamlessly spliced to generate an intact and coherent building function map for each city. In general, this workflow ensured accurate, high-resolution mapping while addressing the challenges posed by the dense and diverse urban environments.

In addiction, we conducted a post-processing step to further optimize map products (see third step of Supplementary Fig. 6). In detail, this part was designed to vectorize the original building function map from raster format to polygons and supplement the building footprints that were incompletely mapped by the model. For every building footprint in the dataset, we first calculated the percentage area occupied by each functional type. If a particular functional type was found to cover more than 50% of the building’s total area, we assigned that type to the entire building footprint. Buildings for which no single functional type occupied more than 50% of the footprint area were labeled as “unmapped,” indicating that their functions could not be confidently determined due to mixed or incomplete class coverage. Subsequently, a lightweight classifier with Mask R-CNN structure62 was employed to categorize unmapped building footprints. Supplementary Fig. 7b further delineates the details of the post-processing step. The structure contains: (1) the geo-prior encoder comprising a fully connected (FC) layer, a rectified linear unit, and a dropout function; (2) the optical encoder comprising a ResNet50 backbone, a feature pyramid network, and a region of interest align function. Following a strategy similar to the SEASONet method, we provided bounding boxes of the building footprints as priors, enabling the model to infer their categories with greater precision. Each building footprint was represented by its bounding box coordinates normalized to the image space, where the top-left corner corresponds to (0, 0) and the bottom-right corner to (1, 1). These normalized coordinates were fed into the geo-prior encoder, enabling the model to infer their categories with greater precision. Finally, all building vectors were merged according to urban administrative divisions to create a cohesive and unified map.

Accuracy validation
Given the importance of accurate mapping for downstream analysis, we conducted a comprehensive evaluation using government reports and in situ validation data outlined in the Data Section. This evaluation comprised two parts. First, a statistical-level evaluation was performed for each city based on official reports from the China Urban-Rural Construction Statistical Yearbook and China Statistical Yearbook. According to the classification system of these statistical reports, eight types of building functions were grouped into four primary categories: “residential,” “commercial and business facilities,” “administration, public services, and municipal utilities,” and “industrial manufacturing, logistics, and warehouse.” Supplementary Fig. 2a presents the details of statistical validation results. Second, a building-level geospatial evaluation was conducted by using 5.28 million field-observed points from Amap Inc., which is the most widely used map application in China for daily navigation with its high accuracy and reliability. Based on that, point-level and building-level accuracies were calculated to compare the in situ points with the mapped buildings at the same location. Before the verification, we selected some POI anchor points confirmed through street view photos and field tours for manual confirmation. Overall, it shows that POIs are strictly aligned with the utilized remote sensing data under the same WGS 1984 projected coordinates system.

Moreover, Supplementary Fig. 9a, c present the detailed information of point sets that were used in both point-level and building-level validation, including the total number of points of each building functional type and each city. Supplementary Fig. 9b illustrates the spatial distribution of validation points in a sample area of Beijing. Specifically, Supplementary Fig. 9d demonstrates a typical example of the POI-based validation process. For point-level validation, each POI point is treated as the smallest verification unit. It is considered a correct sample if its assigned category matches the functional type of the building it belongs to; otherwise, it is an incorrect sample. For building-level validation, each building is treated as the smallest verification unit. If the majority of POIs within a building match its functional type (i.e., their number exceeds the total of all other POI types), the building’s function is considered to be correctly identified.

From the above verification protocols, statistical-level verification reflects the city-scale consistency between the map product and the official government report. Based on the POIs, building-level verification reflects a more practical measure of product accuracy, since if most rooms or POIs within a building serve a particular function, it should be considered as the building’s primary functional type. As a reference, we also calculated the point-level accuracy in a stricter way, which does not ignore or tolerate the inconsistent POIs (e.g., rooms or entities in the same building) serving different functions with the building type. In general, Supplementary Table 2 shows the classification system and accuracy of each functional type at the point, building, and statistical levels.

Multi-level residential environment assessment metrics
To examine the built environment qualities, the nine assessment indicators are grouped into three dimensions: urbanization intensity, facility accessibility, and infrastructure sufficiency.

Dimensions of urbanization intensity include the building height, building density, and nighttime light intensity. For the building height, we utilized the 10-m resolution building height data of China, i.e., CNBH-10m, to extract every building’s height across 109 cities. For the building density, we measured the total building amounts from the produced building function map and divided it by the total administrative area of the city. For the nighttime light intensity, we referenced the boundaries of the GAIA product63 and the statistics of normalized nighttime light intensity (0–255) within those areas, adopting 30 as the threshold to indicate high-intensity human activity. Accordingly, we identified and quantified areas in the SDGSAT-1 nighttime light imagery where the intensity exceeded this threshold. Then, we calculated their proportion compared to the total urban area. Fig. 2a demonstrates the spatial distribution of these indicators.

Dimensions of facility accessibility indicate residents’ ease in accessing specific services or facilities. In detail, there are three main steps to quantify the facility accessibility. (1) Cost matrix construction: based on Weiss’s research on modeling the speed of individuals across the landscape24, we integrated the previously produced land-cover product—SinoLC-1 and traffic network data—OpenStreetMap, assigning time-cost weights to each land use and road type to construct the cost matrix. The time-cost weights were collected from two well-established studies24,64, and the weight values were shown in Supplementary Tables 5 and 6. (2) Facility accessibility calculation: to generate the facility accessibility maps, we obtained the CostDistance function, which calculated the least accumulated cost of each pixel to the nearest facility. (3) Building-level quantification: we calculated the average accessibility for each residential building to assess the inhabitants’ facility accessibility. The spatial analysis tools utilized in this study were implemented using the WhiteboxTools geospatial data analysis platform65. Supplementary Fig. 10 presents several examples of the accessibility maps across 12 cities, while Supplementary Fig. 11 shows the acquisition dates of the high-resolution imagery used to produce the SinoBF-1 and SinoLC-1, which served as the basis for calculating these accessibility maps.

Dimensions of infrastructure sufficiency include indicators of neighborhood amenity diversity, inequality of residence capacity allocation, and per capita infrastructure occupation, which aim to measure both the diversity and adequacy of infrastructures. The details of these indicators are explained in this section. Firstly, diverse neighborhood amenities provide a comfortable and adaptable living environment, addressing the challenges of growing population density and the need for sustainable urban development. Moreover, a neighborhood circle refers to a basic unit within walking distance that meets daily living needs? We evaluated the neighborhood amenity diversity around residential buildings using a 15-min walking distance (1 km buffer), following the Technical Guidelines for Community Life Circle Planning issued by the Ministry of Natural Resources of China66, focusing on two indices: building availability and amenity diversity (Supplementary Fig. 5a, b). Notably, the calculation involves the building types of commercial, administrative, healthcare, sport and art, educational, and public service, where industrial buildings are excluded from the analysis as they contribute little to residential comfort. First, availability reflects the residential and other functional buildings’ density within the 15-min neighborhood circle67. For each residential unit k, the building availability was calculated as follows:

A_k = Σ_{i=1}^{n} NIF_i, (2)

where NIF represent the non-industrial amenity, and n is the number of these amenities. Second, amenity diversity signifies the richness and balance of infrastructures68. We utilized Shannon’s index to quantify the amenity diversity, and the diversity of residential unit k was calculated as follows:

D_k = - Σ_{c=1}^{m} p_c log p_c = - Σ_{c=1}^{m} P_c log P_c, (3)

where m is the number of categories of amenities, p_c and n_c represent the proportion and number of the living amenities within the 15-min neighborhood circle, respectively.

Furthermore, adequate, safe, and affordable housing is a fundamental goal of sustainable urban development. Residence capacity allocation directly reflects the level of equality in urban living conditions69. To quantify this, we applied the Gini coefficient to measure inequality in the distribution of residential building space:

G = 1 - Σ_{t=1}^{T} (P_t - P_{t-1}) (V_t + V_{t-1}), (4)

where t denotes the rank in the cumulative percentage of the population and the building volume, sorted in ascending order by average building space per capita, P_t represents the cumulative proportion of the population, and V_t represents the cumulative proportion of residential building volume allocated by the corresponding group of people. For each grid cell containing multiple residential buildings, we estimated the population allocation to each building by multiplying its residential floor area with the population density of the grid cell, assuming a uniform distribution of residents across residential spaces.

Lastly, infrastructure development is also vital to both household livelihoods and economic activity70. We assessed the infrastructure occupation of residents in cities according to the per capita infrastructure area (I_{pc}):

I_{pc} = (Σ_{i=1}^{n} Area_i) / Pop, (5)

where Pop represents the total population of the city, obtained from the Seventh National Population Census, n denotes the number of specific infrastructure facilities, including healthcare and public service, and Area_i represents the area of each facility.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents a technically impressive and conceptually important advance in the study of urban spatial disparities. By mapping the function of 110 million individual buildings across 109 Chinese cities using 1-meter multi-modal satellite imagery, the authors produce the first building-level, nationwide dataset of urban building functions. The reported accuracy, with overall accuracy of 84%, is satisfactory for a national-scale mapping task. Moreover, upon examining their code repository and raw data, the authors have made their workflow reproducible and transparent. This is particularly important because the methodological framework could be transferable beyond China.\nLeveraging this self-produced dataset, the authors assess spatial disparities across three dimensions: urbanization intensity, facility accessibility, and infrastructure sufficiency. The creation of this high-resolution (1-meter), building-level product enables the study to reveal nuances that previous research, with coarser data, might have missed. For example, the study provides novel insights into how spatial disparities manifest across city tiers, regions, and intra-city zones, highlighting the uneven distribution of public services, the unexpected housing equality in mid-tier cities, and the infrastructure strain in rapidly growing southern cities. The authors also attempt to link these spatial disparities to long-term development policies (e.g., Third-Front Movement, Northeast Revitalization), offering historical and policy context. These findings also offer some practical insights for understanding urban development in China, and bring interest to researches in related fields.\nThe manuscript is clearly written, well-organized, and the validation process demonstrates a reasonable level of rigor.\nOverall, I believe the study makes a valuable contribution in terms of methodology and data, and it is suitable for publication after revisions.\n1.The authors note that the SinoLC-1 dataset (previously developed by the authors and widely known in the Earth-observation community) has served as a foundational basemap for numerous fine-scale studies. Similarly, the building-function dataset and framework presented here are likely to become a benchmark resource for urban research. However, a broader readership, including urban scholars and policymakers, often wishes to apply such models without necessarily having expertise in advanced AI or remote sensing. To maximize accessibility and uptake, I recommend the following improvements. The authors should clarify the algorithmic design by providing more technical details in the Supplementary Materials. In particular, they should explain how the Resolution-Preserving (RP) block and Vision Transformer (ViT) block are constructed within the CNN and ViT branches (Extended Data Fig. 7); why the framework adopts these two parallel branches; and what advantages this dual-branch design offers compared with using either branch alone for feature extraction or mapping. Furthermore, the authors should clarify how the semi-supervised loss function handles background areas, buildings of known categories, and buildings of unknown categories during model training. This clarification is even more important than describing the dataset itself. Once these models are well explained and accompanied by detailed guidance, the proposed framework can be readily deployed by researchers in urban studies to analyze other types of urban infrastructure worldwide.\n2.In Extended Data Fig. 3, the authors present a case study in Shanghai which provides accessibility maps for education, healthcare, and public services by combing the previous SinoLC-1 and the building function map proposed in this study. I consider this integration is also another contribution of the paper, as it demonstrates a practical application of the dataset that directly informs understanding of urban accessibility. However, I strongly encourage the authors, in their revision, to extend the visualization to additional cities, which would help illustrate the broader applicability and robustness of the approach. Furthermore, planning for an open release of the full set of accessibility maps for all 109 cities would greatly enhance the utility and impact of this work. Making these data publicly accessible would allow other researchers to build directly on the analytical framework developed here, facilitate comparative studies, and likely benefit research in human geography and transportation activities in China. Such data sharing would not only increase the transparency of this study but also strengthen its contribution to the wider scientific community.\n3.It would be helpful if the authors could provide additional explanation for two methodological choices. Thresholds such as the 15-minute walking distance and the nighttime light cutoff are central to the analysis, yet their rationale is not fully elaborated. Similarly, the decision to train seven region-specific models appears reasonable, but a short justification for why this number of regional models was selected would further clarify the study design. While these choices do not undermine the robustness of the work, offering brief explanations would improve methodological transparency and strengthen the manuscript’s clarity.\n4.The authors point out that urban cores consistently benefit from the shortest travel times to essential facilities, while those in intermediate zones and peripheries face substantially higher time costs. At the same time, they note that areas with high population density tend to coincide with zones of high facility accessibility. These two observations are closely related and may suggest that population distribution itself could partly explain differences in accessibility across zones. To strengthen the argument, the authors are encouraged to explicitly discuss this relationship and, if possible, provide per-capita measures of accessibility or travel time. This would help clarify whether the observed disparities are primarily a result of spatial zoning or underlying population distribution patterns.\n5.Regarding the authors’ discussion of multiple functions per building in the POI data in Extended Data Fig. 9(d), the authors used 5.28 million in situ points of Amap, the most widely used navigation-map source in China, as their validation source. I suggest that the authors further discuss the point-selection strategy when using this dataset. For example: (a) how they handle POI points that fall outside building footprints; (b) whether any filtering of POI points is necessary to exclude potentially confounding points (e.g., ATMs, public toilets, tourist attractions); and (c) although Extended Data Table 2 presents some POI information for buildings of different functions, I recommend expanding this table in the supplementary material and potentially including street-level images from Amap to show how these POIs and functional categories actually appear on the ground. This would further strengthen the data-validation procedure in the paper.", "reviewer_2": "This manuscript delivers an ambitious, data-intensive mapping of the functional use of ~110 million buildings across 109 Chinese cities at 1-m resolution, and uses that dataset to analyze multi-scale spatial disparities in the built environment. The nine-indicator, three-dimension framework (urbanization intensity, facility accessibility, infrastructure sufficiency) is well motivated and executed, the figures are effective, and validation is described clearly. This work effectively addresses a critical data gap and establishes an analytical framework with potential global relevance. I believe this paper offers sufficient originality and contribution. To further strengthen the manuscript, I have suggestions below for the authors’ consideration in revision.\n1) Please add a brief paragraph, likely in the discussion, that clarifies (i) where results at the three spatial levels (tiers, regions, intra-city areas) converge or diverge, and (ii) how the three dimensions may interrelate (e.g., correlations between urbanization intensity and accessibility/sufficiency). This would make these interdependencies explicit and help readers connect the building-function maps to observed disparities.\n2) The author note that building functions follow national standards and list example POIs (Extended Data Table 2). For clarity and reusability, please add a concise definition table that (a) names each functional class, (b) provides its formal definition from the cited standards, and (c) lists typical POI proxies. This will help readers interpret categories and apply the dataset consistently.\n3) The two-stage approach (semi-supervised pixel segmentation → Mask R-CNN vectorization/classification) is efficient and scalable. But two short clarifications would strengthen transparency:\n• Mixed pixels within a single building: how are conflicting pixel labels reconciled to a single building functon(e.g., majority vote, area-weighted rules, confidence thresholds, mixed-use handling)?\n• Input normalization across modalities: optical, height, and nighttime-light bands have different ranges/dynamics. Please specify how bands were normalized/standardized (per-band z-score, min–max by sensor/region, histogram matching, etc.) before joint training/inference.\n4) Given the effort to assemble the most recent datasets, a compact time-stamp figure (e.g., stacked bars or timeline histograms) showing acquisition dates for optical imagery and nighttime lights (height is less time-sensitive) would help readers assess recency and interpret spatial differences in light of input vintages.\n5) Lines 37–39 list the dimensions and indicators. A short paragraph explaining why these dimensions/indicators were chosen—linking to established practice (e.g., accessibility as a service proxy, diversity as a sufficiency proxy) and practical constraints—would tighten the conceptual bridge between research questions and methods.\n6) Some patterns recur across tiers, regions, and intra-city analyses. Consider moving granular statistics to the Supplementary Information and keeping the main text to the distinctive insights at each scale. This will improve flow without reducing analytical depth.\n7) Lines 85–89 pose two research questions, while the abstract foregrounds three headline findings. Please reconcile these by (a) mapping each main finding to a stated question, or (b) modestly revising the questions so readers can trace the logic from questions → methods → results.\n8) Lines 185–191 define three intra-city areas. Because spatial delineation can influence disparity estimates, please add a short justification (citations, planning practice, or empirical criteria). If feasible, a brief sensitivity note (e.g., similar results under an alternative buffer or density-based boundary) would reinforce robustness.\n9) In Lines 276–280 and Fig. 5, the author average indicators with different ranges/variances. Please clarify the normalization applied prior to aggregation and the rationale for equal weights.", "reviewer_3": "I enjoyed reading this manuscript on mapping building functions to analyze large scale dynamics in urban environments. The argument for using fine-scale building information is well motivated. I agree that such resolution, in addition to the detailed information on building function, can reveal patterns that are not observable at coarser scales or lower levels of detail. Overall, the manuscript is well written and in my view suitable for publication after minor revisions. Most of my comments pertain to focus, conciseness, and placement of certain interpretations.\n________________________________________\nSpecific comments\nLine 38 - The manuscript currently emphasizes that the insights can be used to guide policy and planning. In my view, the discussion provides primarily a historical interpretation of the observed patterns; although relevant, this is broad and not easily actionable at the policy level. I suggest tempering this claim.\nLine 54-57 - This paragraph opening repeats content already stated in the preceding paragraph. Consider shortening or removing to avoid redundancy.\nFigure 3(b) - Consider limiting the x-axis to 0-30 for clarity, and moving the full ‚0 to 120 version to an appendix.\nLine 130-133 - This text provides an interpretation rather than a direct statement of results. It would fit better in the discussion. Alternatively, revise to succinctly summarise only what is shown in the data.\nLine 143 - \"This co-location suggests…\" is another interpretive sentence in the Results section; consider adjusting or removing to maintain focus.\nLine 210 - Remove the final sentence of this paragraph.\nLine 257 - Replace \"unprecedented,\" which is too strong for this context.\nLine 274 - Remove the sentence beginning \"Our building-level…\" as the following sentence conveys the same point.\nLine 297-300 - The sentence beginning \"Despite these limitations…\" is redundant with earlier text. Consider deleting.\nLine 441 - Please clarify whether any geometric correction / co-registration was required, and if not, explain why this was unnecessary.\nLine 448 - Remove the redundant point.\nLine 479-484 - The accessibility information appears to be part of the input data used in the analysis. Why is it listed under evaluation data?\nLine 535 - Please clarify what is meant by \"unmapped buildings.\" Under what circumstances would some buildings be unmapped?\nLine 540 - In what form are the building footprints passed to the geo-prior encoder? (e.g. bounding box coordinates, rasterized masks?)\nLine 585 - On what basis was this threshold selected?\nLine 593 - Were default weights used, or were they adjusted? Please specify."}, "author_response": "Response to the reviewers\nReviewer 1\nThismanuscriptpresentsatechnicallyimpressiveandconceptuallyimportantadvanceinthestudyofurban\nspatial disparities. By mapping the function of 110 million individual buildings across 109 Chinese cities\nusing1-metermulti-modalsatelliteimagery,theauthorsproducethefirstbuilding-level,nationwidedataset\nof urban building functions. The reported accuracy, with an overall accuracy of 84%, is satisfactory for a\nnational-scalemappingtask.\nMoreover, upon examining their code repository and raw data, the authors have made their workflow\nreproducible and transparent. This is particularly important because the methodological framework could\nbe transferable beyond China. Leveraging this self-produced dataset, the authors assess spatial disparities\nacross three dimensions: urbanization intensity, facility accessibility, and infrastructure sufficiency. The\ncreation of this high-resolution (1-meter), building-level product enables the study to reveal nuances that\nprevious research, with coarser data, might have missed. For example, the study provides novel insights\nintohowspatialdisparitiesmanifestacrosscitytiers,regions,andintra-cityzones,highlightingtheuneven\ndistribution of public services, the unexpected housing equality in mid-tier cities, and the infrastructure\n1\nstrain in rapidly growing southern cities. The authors also attempt to link these spatial disparities to long-\nterm development policies (e.g., Third-Front Movement, Northeast Revitalization), offering historical and\npolicy context. These findings also offer some practical insights for understanding urban development in\nChina,andbringinteresttoresearchesinrelatedfields.\nThemanuscriptisclearlywritten,well-organized,andthevalidationprocessdemonstratesareasonable\nlevelofrigor. Overall,Ibelievethestudymakesavaluablecontributionintermsofmethodologyanddata,\nanditissuitableforpublicationafterrevisions.\nReply: Thank you so much for your positive comments. We believed the manuscript is much improved\nafteraddressingyourcomments. Below,pleasefindourpoint-to-pointresponse.\nReviewer Comment1.1 — The authors note that the SinoLC-1 dataset (previously developed by the au-\nthors and widely known in the Earth-observation community) has served as a foundational basemap for\nnumerous fine-scale studies. Similarly, the building-function dataset and framework presented here are\nlikely to become a benchmark resource for urban research. However, a broader readership, including ur-\nban scholars and policymakers, often wishes to apply such models without necessarily having expertise in\nadvanced AI or remote sensing. To maximize accessibility and uptake, I recommend the following im-\nprovements. The authors should clarify the algorithmic design by providing more technical details in the\nSupplementaryMaterials. Inparticular,theyshouldexplainhowtheResolution-Preserving(RP)blockand\nVisionTransformer(ViT)blockareconstructedwithintheCNNandViTbranches(ExtendedDataFig. 7);\nwhytheframeworkadoptsthesetwoparallelbranches;andwhatadvantagesthisdual-branchdesignoffers\ncomparedwithusingeitherbranchaloneforfeatureextractionormapping. Furthermore,theauthorsshould\nclarify how the semi-supervised loss function handles background areas, buildings of known categories,\nand buildings of unknown categories during model training. This clarification is even more important than\ndescribingthedatasetitself. Oncethesemodelsarewellexplainedandaccompaniedbydetailedguidance,\nthe proposed framework can be readily deployed by researchers in urban studies to analyze other types of\nurbaninfrastructureworldwide.\nReply: Thanks for the constructive comments. We appreciate your positive feedback on the SinoLC-1\ndataset,andwedoagreethataddingmoretechnicaldetailsofthemappingframeworkcouldhelpreadersor\nusers with their mapping task. The core mapping framework used in this study is called ”Paraformer”, an\nefficient, weakly supervised framework to guide large-scale HR mapping with easy-access, historical, and\nweaklabels. Thisframeworkwasfirstproposedbyusin2024[R1], whichhasbeenusedtoconductreliable\nlarge-scaleland-covermapsinChina(i.e.,SionLC-1),theUS,andPoland.\nToexplaintheframeworkmorespecifically,weaddressedeachquestionasbelow:\n(1)HowtheResolution-Preserving(RP)blockandVisionTransformer(ViT)blockareconstructedwithin\ntheCNNandViTbranches?\nToexplainitindetail,weintroducethesetwobranchesandtheirinternalblocksonebyone. First,theCNN\nbranchisdesignedtocapturelocalcontextsfromHRimagesandpreservethespatialdetailsbypreventing\nfeature downsampling. As shown in Fig. R1, the CNN branch is constructed by five serially connected\nresolution-preserving (RP) blocks. Each RP block contains parallel convolution layers with the sizes of\n1×1,3×3,and5×5,whosestepsaresetto1forfeaturesizemaintenance. Partlysimilartotheinception\nmodule[R2],thechannelnumbersofdifferentscales’layersineachblockareinverselyproportionaltotheir\nkernelsizes,whicharesetto128,64,and32. Basedonthesetting,theRPblockscancapturefeatureswith\na proper receptive field instead of downsampling the feature maps with a deep encoder-decoder pattern.\nThe serial blocks aim at sufficiently preserving the spatial resolution of features by using the majority of\n1×1 kernels. The 3×3 and 5×5 kernels capture necessary surrounding information. Furthermore, the\n2\nmulti-scalefeaturemapsareconcatenatedandreducedto128channelsforthebranchlightning. Besides,a\nshortcutconnectionisadoptedbetweenblocksforresiduallearninganddetailpreserving.\nSecond, by considering that the CNN branch with intrinsic locality hinders the adaptation of various\nlandformsoverlarge-scaleareas,wefurtherhybridtheCNNbranchwithaTransformerbranch,whichaims\natcapturingglobalcontextsandbuildinglong-rangesupportamongdispersedgeographicareas. Asshown\ninFig. R2,theTransformerbranchcontains12transformerlayers. Eachlayerincludeslayernormalization,\nmulti-head self-attention, and multi-layer perception. The feature maps extracted by each RP block are\nconcatenated and inputted to the Transformer branch. Specifically, the extracted features from the CNN\nbrancharedownsampledandembeddedinahiddenfeaturelayer. AndthentheTransformerbranchencodes\nthe dense feature patches to capture global contexts. Subsequently, the encoded features are constantly\nupsampled to the size of HR images and classified into the final results. During the upsampling process,\ntheoutputfeaturesofeachstageareconcatenatedwiththepre-encodedfeatures,whichbringmassivelocal\ncontextualinformationtothefinalfeaturemaps.\nFigureR1: AnillustrationofanRPblock. TheinputmapI(b) issampledbythreeconvolutionallayerswithsizesof\n1×1,3×3,and5×5,andtheconvolutionkernelsineachlayeraresettotheproportionofω forpreventingfeature\nresolutionreductioncausedbytheoverdownsampling.\n(2) Why does the framework adopt these two parallel branches? And what advantages does this dual-\nbranchdesignoffercomparedwithusingeitherbranchaloneforfeatureextractionormapping?\nAs the advantages of CNN and Vision Transformer architectures are introduced above, the main reason\nfor combining two parallel branches is summarized as follows: In the Paraformer, a downsampling-free\nCNN branch is parallelly hybridized with a Transformer branch to capture features with both high spatial\nresolutionanddeep-levelrepresentation. Thestructureaimstogloballyadaptlarge-scale,variouslandforms\nacross China and locally preserve high-resolution (1-m) ground details. In our previous study, through\nablation experiments,we found that these two parallel branches can jointly construct a more robust feature\nextractortobridgelocalandglobalcontextualinformation. Fig. R3showsanablationexperimentthatwas\nconductedinourpreviousstudy. ItshowsthevisualizedcontextscapturedbytheCNNbranch,Transformer\nbranch, and hybrid structure (two parallel branches). Fig. R3 (b) indicates that the CNN branch mostly\nfocusesoncapturinglocaldetails(e.g.,theedgesofroads,singlehouses,andshrubs). Fig. R3(c)indicates\nself-attention(MSA),andmulti-layerperception(MLP).\nthattheTransformerbranchcapturesthefeatureinobjectscale, focusingonintactlandobjectsofbuilding\nareasandparkingspots. Thehybridstructureshowsastrongresponsetotheobviousobjectswithbothfine\nedgesandintactareas.\n(3)Howthesemi-supervisedlossfunctionhandlesbackgroundareas,buildingsofknowncategories,and\nbuildingsofunknowncategoriesduringmodeltraining\nDuring the training process, we first combined the multi-modal data as the input cubes, which is shown in\nSupplementary Fig. 7 and Fig. R4(a). Then we used the AOI data collected from the OpenStreetMap and\nthebuildingfootprintdatatogeneratethetraininglabels. Ingeneral,theAOIdatasetonlycoversabout30%\nof the urban area, while the building masks entirely cover the area. Therefore, as shown in Fig. R4(b), the\nfootprint labels include every building object within the study area, but only a small number of them have\nfunction-labeledinformation. Inourpreviousstudy,wedevelopedastablestrategytodealwithbackground\nareas,buildingsofknowncategories,andbuildingsofunknowncategoriesseparately[R3].\nBasedonweaklabels,weadoptasemi-supervisedstrategyforthetrainingprocess. Indetail,wemodify\nthecross-entropy(CE)lossbyconsideringthebackgroundandallfunctiontypesasthesupervisedclasses.\nThegeneralprocessisshowninFig. R4(c). Then,buildingobjectswithoutfunction-labeledinformationare\nregarded as unsupervised classes, which are ignored during loss calculation. Formally, for a training patch\nofthesizeofW×H,weusedY′,Yˆ,andGtorepresentthelabels,results,andtheunlabeledbuildingmask,\nrespectively. ThemodifiedCElosscanbewrittenas:\n(cid:20) (cid:21)\nW H L\n− ∑ ∑ g ij ∑ y′ ij (l)log(yˆ ( i l j ) )\nL (Y′,Yˆ,G)= i=0j=0 l=1 . (1)\nMCE\nSum(G(i,j)=1)\nInEq. 1,y′ (l) andyˆ (l) denotethelabelY′ andpredictionYˆ withtheclassl ofthepixel(i,j). Ifthepixel\nij ij\nisabuildingobjectwithoutfunction-labeledinformation,theg issetto0,otherwisetheg issetto1.\nij ij\nIn the manuscript, more technical details mentioned above have been included in the Method section\n(Line 424–429 and 432–439 of the revised manuscript) to help readers from other fields better understand\nandutilizeourParaformerframework.\n4\n[REDACTED]\nFigure R3: Demonstration of the extracted contexts from the ablation methods. (a) The original HR image. (b)\nThecontextsextractedbythesoleCNNbranch. (c)ThecontextsextractedbythesoleTransformerbranch. (d)The\ncontextsextractedbytheCNN-Transformerhybridbackbone.\n[REDACTED]\nFigure R4: DemonstrationoftheParaformer’strainingprocess. (a)Themulti-modalinputdata. (b)Thegeneration\noftraininglabels. (c)Thesemi-supervisedstrategywasadoptedtodealwithbackgroundareas, buildingsofknown\ncategories,andbuildingsofunknowncategories.\n[R1] Li, Z., He, W., Li, J., Lu, F., and Zhang, H., 2024. Learning without exact guidance: Updating\nlarge-scale high-resolution land cover maps from low-resolution historical labels. In Proceedings of the\nIEEE/CVFConferenceonComputerVisionandPatternRecognition(pp. 27717-27727).\n[R2]Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V.andRa-\nbinovich, A., 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer\nvisionandpatternrecognition(pp. 1-9).\n[R3]Li,Z.,He,W.,Li,J.,andZhang,H.,2024,July. Identifyingeverybuilding’sfunctioninlarge-scaleur-\nbanareaswithmulti-modalityremote-sensingdata. InIGARSS2024-2024IEEEInternationalGeoscience\nandRemoteSensingSymposium(pp. 310-314). IEEE.\nReviewer Comment1.2 — In Extended Data Fig. 3, the authors present a case study in Shanghai which\nprovidesaccessibilitymapsforeducation,healthcare,andpublicservicesbycombingthepreviousSinoLC-\n1andthebuildingfunctionmapproposedinthisstudy. Iconsiderthisintegrationisalsoanothercontribution\nof the paper, as it demonstrates a practical application of the dataset that directly informs understanding of\nurbanaccessibility.\nHowever, I strongly encourage the authors, in their revision, to extend the visualization to additional\ncities, which would help illustrate the broader applicability and robustness of the approach. Furthermore,\nplanning for an open release of the full set of accessibility maps for all 109 cities would greatly enhance\ntheutilityandimpactofthiswork. Makingthesedatapubliclyaccessiblewouldallowotherresearchersto\nbuilddirectlyontheanalyticalframeworkdevelopedhere,facilitatecomparativestudies,andlikelybenefit\nresearch in human geography and transportation activities in China. Such data sharing would not only\nincreasethetransparencyofthisstudybutalsostrengthenitscontributiontothewiderscientificcommunity.\nReply: Thankyouforyoursuggestions. Wecombinedtwofine-grainedmapproducts(i.e.,SinoLC-1and\nSinoBF-1) that we produced to generate the accessibility maps. To better visualize more results among\ndifferent cities, we have now extended the visualization of facility accessibility maps to twelve additional\nrepresentativecitiesinSupplementaryFig.10(showninFig. R5ofthisresponseletter),includingShenzhen,\nGuangzhou, Wuhan, Tianjin, Xi’an, Taiyuan, Harbin, Luoyang, Lanzhou, Jilin, Dongying, and Panzhihua.\nFurthermore, we have open-sourced the code for calculating accessibility using any building function and\nland-cover data. Since all our map data and algorithms are publicly available, users can easily generate\naccessibility maps for all 109 cities using the project repository of this study: https://github.com/\nintelligent-bee/Sino_BFmap/.\n≤1\nn\no\nita\nc\nu\nd\nE\n≥ 120\nmins\n≤1\ne\nra\nc\nh\ntla\ne\nH\n≥ 120\nmins\n≤1\ne\nc\niv\nre\ns\nc\nilb\nu\nP ≥ 120\nmins\nHarbin Luoyang Lanzhou Jilin Dongying Panzhihua\n≤1\nn\no\nita\nc\nu\nd\nE\n≥ 120\nmins\n≤1\ne\nra\nc\nh\ntla\ne\nH\n≥ 120\nmins\n≤1\ne\nc\niv\nre\ns\nc\nilb\nu\nP ≥ 120\nmins\nFigureR5: Spatialdistributionmapsofaccessibilitytoeducation,healthcare,andpublicservicesin12representative\ncities,withtraveltimerangingfrom1to120minutes.\nReviewerComment1.3 — Itwouldbehelpfuliftheauthorscouldprovideadditionalexplanationfortwo\nmethodological choices. Thresholds such as the 15-minute walking distance and the nighttime light cutoff\nare central to the analysis, yet their rationale is not fully elaborated. Similarly, the decision to train seven\nregion-specificmodelsappearsreasonable,butashortjustificationforwhythisnumberofregionalmodels\nwas selected would further clarify the study design. While these choices do not undermine the robustness\nof the work, offering brief explanations would improve methodological transparency and strengthen the\nmanuscript’sclarity.\nReply: Thanks for the comments. According to your suggestions, we have added these explanations one\n7\nbyonebelow:\n(a)15-minutewalkingdistance\nDue to the scale effect and the differences in residents’ daily travel behaviors, mobility patterns, and age\nstructures,thebufferthresholdfordefiningdaily-lifecirclesvariesacrossstudies. Toensurecomparability\nandfairnessamongdifferentChinesecities,weadoptedaunifiedthresholdof1000m,followingtheTech-\nnicalGuidelinesforCommunityLifeCirclePlanningissuedbytheMinistryofNaturalResourcesofChina.\nWehaveaddedanexplanationinthemanuscript:\n“Weevaluatedtheneighborhoodamenitydiversityaroundresidentialbuildingsusinga15-minutewalk-\ning distance (1 km buffer), following the Technical Guidelines for Community Life Circle Planning issued\nby the Ministry of Natural Resources of China, focusing on two indices: building availability and amenity\ndiversity(SupplementaryFig. 5a,b).”(Lines543–546)\n(b)Thresholdsofthenighttimelightintensitycutoff\nBefore the analysis, we reviewed extensive studies on urban nighttime light and identified the Global Arti-\nficialImperviousArea(GAIA)dataset[R4],developedbytheUniversityofHongKong,ashighlyrelevantto\nour work. The GAIA product was generated and validated using nighttime light data and manual interpre-\ntation,makingitarobustreferenceforourthresholddetermination.\n[REDACTED]\nFigureR6: DemonstrationoftheSGDSAT-1nighttimelightdata,GAIAboundary(inred),andhistogramofnormal-\nizednighttimelightintensitiesinthedemonstratedarea.\naries, which clearly delineate human settlements with high light intensity. By analyzing the normalized\nnighttime light intensity (0–255) within GAIA-defined areas, we obtained an average value of approxi-\nmately30. AsshowninFig. R9(c),athresholdof30effectivelyexcludesdarkregionslackingdensehuman\nactivity.\nAccordingly,followingpreviousresearchandourdefinitionofhigh-intensityactivityareas,weadopted\n30asthethresholdinthisstudy. Wehavesupplementedamoredetaileddescriptionin: “Forthenighttime\nlightintensity,wereferencedtheboundariesoftheGAIAproductandthestatisticsofnormalizednighttime\nlight intensity (0–255) within those areas, adopting 30 as the threshold to indicate high-intensity human\nactivity. Accordingly,weidentifiedandquantifiedareasintheSDGSAT-1nighttimelightimagerywherethe\nintensityexceededthisthreshold.”(Lines515–519)\n(c)Thedecisiontotrainsevenregion-specificmodels\nFigureR7: Demonstrationoftheregionaldivisionofseventrainingregions.\nFor the explanations of the decision to train seven region-specific models, we continued to adopt the\nsettings used in SinoLC-1, which have been validated as stable and reliable in previous 1-m land-cover\nmapping. In detail, considering the vast extent of China’s territory and its diverse landforms, as well as\nvariations in geographic location, climate, economic development, and land-cover patterns, the country’s\nland surfacewas divided intoseven geographical regionsfor separatetraining. Such a strategywas widely\nused in previous studies of China’s Urban mapping and analysis[R5,R6,R7]. Fig. R7 illustrates the locations\nandboundariesoftheseregions: East,Northeast,North,Northwest,Central,South,andSouthwest.\nIn summary, regarding the three points you mentioned, we have provided detailed explanations in the\nMethodssectiontoenhancethetransparencyofthemethodologypresentedinthisstudy.\nReferenceofthisreply:\n[R4] Li, X., Gong, P., Zhou, Y. et al. (2020). Mapping global urban boundaries from the global artificial\nimperviousarea(GAIA)data. EnvironmentalResearchLetters,15(9),094044.\n[R5] Lin, G.C., 2002. The growth and structural change of Chinese cities: a contextual and geographic\nanalysis. Cities,19(5),pp.299-316.\n[R6]Ning,Y.,Liu,S.,Zhao,S.,Liu,M.,Gao,H.andGong,P.,2022. Urbangrowthrates,trajectories,and\n9\nmulti-dimensionaldisparitiesinChina. Cities,126,p.103717.\n[R7] Li, Z., He, W., Cheng, M., Hu, J., Yang, G. and Zhang, H., 2023. SinoLC-1: the first 1 m resolution\nnational-scaleland-covermapofChinacreatedwithadeeplearningframeworkandopen-accessdata. Earth\nSystemScienceData,15(11),pp.4749-4780.\nReviewer Comment1.4 — The authors point out that urban cores consistently benefit from the shortest\ntraveltimestoessentialfacilities,whilethoseinintermediatezonesandperipheriesfacesubstantiallyhigher\ntimecosts. Atthesametime,theynotethatareaswithhighpopulationdensitytendtocoincidewithzones\nof high facility accessibility. These two observations are closely related and may suggest that population\ndistributionitselfcouldpartlyexplaindifferencesinaccessibilityacrosszones. Tostrengthentheargument,\ntheauthorsareencouragedtoexplicitlydiscussthisrelationshipand,ifpossible,provideper-capitameasures\nofaccessibilityortraveltime. Thiswouldhelpclarifywhethertheobserveddisparitiesareprimarilyaresult\nofspatialzoningorunderlyingpopulationdistributionpatterns.\nReply: Thankyouforyourinsightfulandconstructivecomments. Followingyoursuggestion,wehavein-\ncorporatedanadditionalanalysisusingper-capitameasuresofaccessibilitytofurtherclarifytherelationship\nbetweenpopulationdistributionandfacilityaccessibility. AsillustratedinFig. R8,introducingpopulationas\nthedenominatoryieldsresultsthatresultsthatarefullyconsistentwithourmainfindings. Boththebuilding-basedand\npopulation-basedaccessibilitymeasuresconsistentlyrevealthepronouncedadvantagesoffirst-tiercities.\nEducation Healthcare Public service\nFigure R8: Cumulative accessibility curves (0–35 mins) showing the travel time required for different population\nproportionstoreachthenearesteducational,healthcare,andpublicservicefacilities.\nForexample,infirst-andsecond-tiercities,withouttakingintoaccountthetimeresidentsspendgoing\nupanddownthestairs(thetimecostintheverticaldirection),50%ofthepopulationcanreachthenearest\neducational facility within approximately 3.2 minutes, whereas the corresponding travel time in fifth-tier\ncities is about 8.6 minutes. These stable and comparable patterns demonstrate that, regardless of whether\naccessibilityisassessedbasedonbuildingdistributionorpopulationdistribution,first-tiercitiesconsistently\nexhibit significantly higher accessibility levels. This further confirms the robustness and reliability of our\nconclusions. Intherevisedmanuscript,wehaveaddedthediscussionbasedonthisresultinlines144–146.\nReviewer Comment1.5 — Regarding the authors’ discussion of multiple functions per building in the\nPOIdatainExtendedDataFig. 9(d),theauthorsused5.28millioninsitupointsofAmap,themostwidely\nused navigation-map source in China, as their validation source. I suggest that the authors further discuss\nthe point-selection strategy when using this dataset. For example: (a) how they handle POI points that\nfall outside building footprints; (b) whether any filtering of POI points is necessary to exclude potentially\nconfoundingpoints(e.g.,ATMs,publictoilets,touristattractions);and(c)althoughExtendedDataTable2\n10\npresentssomePOIinformationforbuildingsofdifferentfunctions,Irecommendexpandingthistableinthe\nsupplementary material and potentially including street-level images from Amap to show how these POIs\nand functional categories actually appear on the ground. This would further strengthen the data-validation\nprocedureinthepaper.\nReply: These are great points. To make our validation process clearer, we have supplemented the sample\npointvalidationsectionofthisstudybasedonthepointsyousuggested.\n(a)HowtheyhandlePOIpointsthatfalloutsidebuildingfootprints\nSince the POI data were directly collected from the current version of the Amap mobile app, they\ncontain a large number of samples that do not correspond to buildings (e.g., mobile container shops), as\nwell as points that are located outside buildings due to coordinate offsets (e.g., stores along streets may be\npositionedontheroadratherthanwithinthebuilding). Therefore,wefirstremovedtheseinterferingpoints\nbyconsideringthecoverageofthePOIpointswithbuildingmasks,asshowninSupplementaryFig. 9(b).\n(b) Whether any filtering of POI points is necessary to exclude potentially confounding points (e.g.,\nATMs,publictoilets,touristattractions)\nIndeed,wefoundthatthePOIdataincludemanysamplepointsthatarenotdirectlyrelatedtobuilding\nfunctions. Taking Shanghai as an example, numerous parking lots, bus stations, and public toilets are\nincludedintheAmapdata. OurapproachwastofirstsummarizeallcombinationsofthePOIdataatmajor\nandsub-categorylevels,andthenremoveallpointswhosesub-categoryisunrelatedtobuildingfunctions.\n(c)AlthoughSupplementaryTable2presentssomePOIinformationforbuildingsofdifferentfunctions,\nI recommend expanding this table in the supplementary material and potentially including street-level\nimagesfromAmap\nBased on your suggestions, we have expanded the list of actual POI types in Supplementary Table 2\n(Table R1 in this response letter) and, taking Shanghai as an example, provided street-view images from\nAmap for each building function type. As shwon in Fig. R9, we selected 16 representative street-view\nimagesbasedonPOIinformationtoreflectdifferentbuildingfunctions. Tofacilitateannotationandprotect\nresidential privacy, we only labeled the roads captured in each image. By demonstrating how these POIs\nandfunctionalcategoriesactuallyappearontheground,itmayfurthersupplementthedetailsofpoint-level\nvalidationprocedureinthemanuscript.\n11\n[REDACTED]\nFigureR9: Demonstrationofthestreet-viewphotosfromapublicmapapplication. Foreachbuildingfunctiontype,\ntheclassnamesareonthetop,whilethePOIlocation(road)islistedbeloweachstreet-viewphoto.\nInthemiddlecolumns,wedemonstratedseveralsamplesandtheaccuracyofeachclassbasedon5.28millionAmap\nvalidationpoints. Inthelastcolumn, wedemonstratedtheunifiedclassofofficialreportsfromtheNationalBureau\nof Statistics and the Ministry of Housing and Urban-Rural Development of China, followed by the corresponding\nR-squaredvalues.\nBuildingfunction AmapPOIclass Point-levelaccuracy Building-levelaccuracy Statisticalclass R2\nResidentialdistrict\nVilladistrict\nDomesticpremises\nResidential 81.93% 90.26% Residential 0.9167\nDormitory\nAccommodation\n...\nConveniencestore\nShoppingmall\nSupermarket\nCompany\nCommercial and business\nCommercial Coffeeshop 68.39% 76.01% 0.7559\nfacilities\nHotel\nMarket\nFoodandBeverage\n...\nCourt\nGovernmentofficebuilding\nBureauofpublicsecurity\nAdministrative 63.36% 71.43%\nCustoms\nProcuratorate\n...\nSpecializedhospital\nPharmaceuticalsales\nFirstaidcentre\nGeneralhospital\nHealthcare 80.25% 80.70%\nDiseaseprevention\nGeneralhospital\nClinic\n...\nFitnessCenter\nCulturalpalace\nMuseum Administration, public ser-\n0.8219\nSportandart Memorialhall 68.66% 72.55% vices,andmunicipalutilities\nSportscenter\nStadium\n...\nUniversity\nSchool\nKindergarten\nEducational College 81.85% 82.01%\nLaboratory\nInstitute\n...\nPostoffice\nCommunitycenter\nServicestation\nGasstation\nPublicservice 72.97% 75.67%\nSanitationstation\nBeadhouse\nPowersupplystation\n...\nWarehouse\nIndustrialbuilding Industrial manufacturing;\nIndustrial 71.25% 77.34% 0.7885\nFactory Logisticsandwarehouse\n...\nOverallresult Overallaccuracy 76.08% 84.65% AverageR2 0.8208\ntheirworkflowreproducibleandtransparent.\nReply: Weappreciateyourrecognitionoftheseefforts. Promotingopenandreproducibleresearchinurban\nscienceresonateswithourgoalofthisstudy.\nReviewer 2\nThismanuscriptdeliversanambitious,data-intensivemappingofthefunctionaluseof 110millionbuildings\nacross 109 Chinese cities at 1-m resolution, and uses that dataset to analyze multi-scale spatial disparities\ninthebuiltenvironment. Thenine-indicator,three-dimensionframework(urbanizationintensity,facilityac\ncessibility,infrastructuresufficiency)iswellmotivatedandexecuted,thefiguresareeffective,andvalidation\nisdescribedclearly.\nThisworkeffectivelyaddressesacriticaldatagapandestablishesananalyticalframeworkwithpotential\nglobalrelevance. Ibelievethispaperofferssufficientoriginalityandcontribution. Tofurtherstrengthenthe\nmanuscript,Ihavesuggestionsbelowfortheauthors’considerationinrevision.\nReply: We greatly appreciate the time and effort you dedicated to reviewing our manuscript. We have\ncarefully revised the paper to address all the points raised. Please find our point-to-point response in the\nfollowing.\nReviewer Comment2.1 — Please add a brief paragraph, likely in the discussion, that clarifies (i) where\nresults at the three spatial levels (tiers, regions, intra-city areas) converge or diverge, and (ii) how the three\ndimensionsmayinterrelate(e.g., correlationsbetweenurbanizationintensityandaccessibility/sufficiency).\nThis would make these interdependencies explicit and help readers connect the building-function maps to\nobserveddisparities.\nReply: We thank the reviewer for this insightful suggestion. In response, we have added a new paragraph\nintheDiscussionsectionthatexplicitlyaddressestheinterdependenciesacrossspatialscalesandindicators.\nThisadditionmakestheserelationshipsexplicitandhelpsreadersbetterconnectthebuilding-functionmaps\ntotheobserveddisparities.\nIn the revised manuscript, we supplement a paragraph to integrate the findings of different levels and\nevaluation dimensions: “Our results reveal coherent patterns across scales and indicators. Urban cores\nand higher-tier cities in the eastern and southern regions exhibit shorter travel times and greater amenity\ndiversity, whereas lower-tier cities in the northern and western regions and later-expanding urban areas\nshowpooreraccessibilityandlowerdiversity. Divergencesareequallyinformative. Mid-tiercitiesdisplay\nmoreequitablehousingallocationthantoptiers,andTier-2citiescombinethehighestbuildingdensitywith\nthelowestmodalheight,indicatingadensity–heightdecoupling. Urbanizationintensitygenerallyco-varies\npositively with facility accessibility and with amenity diversity, but relates negatively to per-capita infras\ntructure provision. These cross-scale relationships underscore how urbanization drives spatial disparities\ninChinesecities.”(Lines294–301).\nexample POIs (Supplementary Table 2). For clarity and reusability, please add a concise definition table\nthat(a)nameseachfunctionalclass,(b)providesitsformaldefinitionfromthecitedstandards,and(c)lists\ntypicalPOIproxies. Thiswillhelpreadersinterpretcategoriesandapplythedatasetconsistently.\nReply: Wethankthereviewerforthesuggestiontoadddefinitionsforeachbuildingcategory. Inresponse,\nweconsideredseveralnationalstandardsonwhichthisstudyisbased,aswellassomeregionallyestablished\nstandards, and information in Supplementary Table R2. Following your recommendation, we added the\nadditional information and formal definition in separate columns for each category. This table (Table R2\nin this response letter) has also been included in the supplementary materials of the revised manuscript\n(SupplementaryTable4).\nLegend Function Definition Typicalsubclass\nResidentialdistrict\nResidentialbuildingsrefertoastructureanditssupportingfa-\nResidential\ncilitiesdesignedforpeople’sdailyliving. Thiscategoryin-\nVilladistrict\nDomesticpremises\nplexes,semi-detachedvillas,andself-builthousesfororiginal\nDormitory\nruralresidents.\nAccommodation\nConveniencestore\nShoppingmall\nCommercial buildings are designed for activities related to\nSupermarket\ncommerce and service industries. It accommodates various\nCompany\nbusinessoperationssuchasretail,offices,hotels,andentertain-\nmentvenues. Thiscategoryincludescommercialcomplexes,\nHotel\nofficebuildings,serviceindustryfacilities,andtourism-related\nMarket\nbuildings.\nFood\nBeverage\nAdministrative buildings serve as the office space for gov- Court\nernmentagencies,politicalorganizations,andrelatedinstitu-\nGovernment office build-\ntions.ThisincludesofficesoftheNationalPeople’sCongress,\ning\ntheChinesePeople’sPoliticalConsultativeConference,courts,\nBureauofpublicsecurity\nprocuratorates,publicsecuritybureaus,firedepartments,cus-\nCustoms\ntoms,andothergovernmentalbodies.\nProcuratorate\nHealthcare buildings are designated for medical and public Specializedhospital\nhealthservices. Itincludeshospitals,outpatientdepartments,\nPharmaceuticalsales\nclinics,centersfordiseasepreventionandcontrol,healthsu-\nFirstaidcentre\npervisioncenters,chronicdiseasetreatmentinstitutions,emer-\nGeneralhospital\ngencycenters,blooddonationcenters,andfamilyplanningser-\nDiseaseprevention\nvicecenters.\nClinic\nFitnesscenter\nSportandartbuildingsencompassculturalandrecreationalfa-\nCulturalpalace\ncilities such as libraries, exhibition centers, museums, radio\nMuseum\nMemorialhall\nvariousculturalactivitycenters. Theyalsoincludesportsfa-\nSportscenter\ncilitiessuchasstadiums,arenas,andtrainingbases.\nStadium\nUniversity\nEducationbuildingsincludefacilitiesforacademic,research,\nSchool\nandtrainingpurposes. Thiscategorycoversuniversities,col-\nKindergarten\nCollege\nschools,aswellasinstitutionsforscientificresearch,survey-\nLaboratory\ning,andtesting.\nInstitute\nPostoffice\nCommunitycenter\nPublicservicebuildingsprovideessentialcommunityandin-\nServicestation\nfrastructureservices.Theseincludepostalfacilities,utilitysta-\ntions, telecommunications centers, sanitation services, water\nSanitationstation\nconservancybuildings,andothermunicipalsupportfacilities.\nBeadhouse\nPowersupplystation\nWarehouse\nIndustrialbuildingsareusedforproduction,research,andlo-\nIndustrialmanufacturing\ngisticsrelatedtoindustry.Itincludesfactories,researchplants,\nheadquarters,manufacturingareas,andwarehousesforstorage\nIndustrialbuilding\nanddistribution.\nFactory\nCNN vectorization/classification) is efficient and scalable. But two short clarifications would strengthen\ntransparency: • Mixed pixels within a single building: how are conflicting pixel labels reconciled to a\nsingle building function (e.g., majority vote, area-weighted rules, confidence thresholds, mixed-use han-\ndling)? • Input normalization across modalities: optical, height, and nighttime-light bands have different\nranges/dynamics. Please specify how bands were normalized/standardized (per-band z-score, min–max by\nsensor/region,histogrammatching,etc.) beforejointtraining/inference.\nReply: Weappreciatethereviewer’sinsightfulcomments. Theclarificationsofyoursuggestionsarelisted\nbelow:\n(a)Reconciliationofmixedpixellabelswithinindividualbuildings\nTo resolve potential conflicts arising from mixed pixel labels within a single building footprint, we\nadopted a majority voting rule combined with a confidence threshold criterion. Specifically, if a particular\nfunctionalcategoryoccupiedmorethan50%ofthebuilding’stotalareaandsimultaneouslyrepresentedthe\ndominantlabeltype,thatcategorywasassignedasthebuilding’soverallfunctionalclass. Incaseswhereno\nsingle category exceeded the threshold, the building was assigned to the class with the highest aggregated\nconfidencescorederivedfrompixel-levelpredictions.\n(b)Normalizationacrossmultimodalinputs\nTo maintain comparability across heterogeneous data sources, we applied consistent normalization\nstrategiesacrossbothstagesofthemodel. Buildingheightandnighttime-lightbandswerenormalizedusing\nmin–max scaling to the range [0, 1], which preserves their relative magnitudes while mitigating sensor-\nspecific bias. Optical bands were standardized via z-score normalization (mean 0, standard deviation 1) to\naccountforilluminationandseasonalvariations.\nWesupplementtheclarificationsintherevisedmanuscript: “Priortointegration,theheightandnight-\ntimelightbandswerenormalizedto[0,1]usingmin–maxscaling,andtheopticalbandswerestandardized\nviaz-scorenormalizationtoreduceilluminationandseasonaleffects.”(Lines406–408)\nReviewer Comment2.4 — Given the effort to assemble the most recent datasets, a compact time-stamp\nfigure(e.g.,stackedbarsortimelinehistograms)showingacquisitiondatesforopticalimageryandnighttime\nlights (height is less time-sensitive) would help readers assess recency and interpret spatial differences in\nlightofinputvintages.\nReply: Wethankthereviewerfortheirattentiontothetemporalsequenceofhigh-resolutionopticalimagery\nand for emphasizing the importance of imagery acquisition timing in interpreting spatial results. Since the\nimagery used in this study is inherited from our previously developed SinoLC-1 dataset, we provide a\ndetailed explanation by referring to the validation approach for image temporal sequences presented in the\nSinoLC-1study.\nWecarefullyexaminedthetemporaldistributionofthehigh-resolutionopticalimagesusedinourstudy.\nFig. R10 in the revised manuscript provides a compact visualization (stacked bar chart or timeline his-\ntogram) showing the acquisition years of all optical imagery across China. Most images were acquired\naround 2021, ensuring that the majority of the dataset reflects recent conditions. However, earlier images\nareprimarilydistributedinnorthernandnorthwesternregions,includingthenorthernborderofInnerMon-\ngolia,partsofGansu,andthesouthernTarimBasininXinjiang. Toassessthepotentialimpactoftheseolder\nimages,weanalyzedland-coverchangeintheseregionsoverthepastdecadeusing30mannualland-cover\ndatasets[R8]. As shown in Fig. R11, we found that areas with older images generally experienced minimal\nchanges (1–3%), while regions such as Tibet and Qinghai showed even lower change rates (≤1%). This\n17\nindicatesthatthetemporalheterogeneityoftheimageryisunlikelytosignificantlybiasourresults,asolder\nimagery coincides with regions of low land-cover dynamics. For nighttime light and height data, the tem-\nporalsensitivitydiffers. Nighttimelightimagesaremostlyfrom2021,similartotheopticalimagery,while\nheightdataislargelytime-insensitive. Byintegratingtheseobservations,thevisualizationandaccompany-\ninganalysisclarifytherecencyofourdatasetsandprovideabasisforinterpretingspatialdisparitiesinlight\nofinputvintages. Thisapproachindicatesthat,whilesometemporalvariabilityexistsintheinputimagery,\nthe overall spatial patterns in urbanization intensity, facility accessibility, and infrastructure sufficiency are\nexpected to remain largely reliable. In the revised manuscript, we have added the figure to Supplementary\nFig. 11(showninFig. R10ofthisresponseletter)andadetailedexplanationinlines532–534.\nReferenceofthisreply:\n[R8]Yang, J.andHuang, X., 2021. 30mannuallandcoveranditsdynamicsinChinafrom1990to2019.\nEarthSystemScienceDataDiscussions,2021,pp.1-29.\nFigure R10: Demonstration of the image capture time and the number of image tiles in different years. The\nproducedSinoBF-1sharedthesamehigh-resolutionimagesourceastheSinoLC-1.\nFigure R11: Demonstration of the regional division of seven training regions. (a) The 30 m annual land-cover\nchangeinChinafrom2011to2021and(b)Theprovincial-scaleland-coverchangerate(2011–2021)inChina.\nwhy these dimensions/indicators were chosen—linking to established practice (e.g., accessibility as a ser-\nviceproxy,diversityasasufficiencyproxy)andpracticalconstraints—wouldtightentheconceptualbridge\nbetweenresearchquestionsandmethods.\nReply: We thank the reviewer for this constructive suggestion. The selection of the three dimensions\n(e.g., urbanization intensity, facility accessibility, and infrastructure sufficiency) and their corresponding\nindicatorsfollowsbothconceptualrationaleandestablishedpracticesinurbanenvironmentassessment.\nFirst, urbanization intensity reflects the physical concentration and activity level of urban areas, which\nare fundamental determinants of environmental and socioeconomic dynamics. Indicators such as building\nheight, building density, and nighttime light intensity jointly capture the vertical and horizontal expansion\nofthebuiltenvironmentaswellashumanactivityintensity. Theseindicatorsarewidelyusedasproxiesfor\nurbanizationandspatialdevelopmentinremote-sensing-basedurbanstudies.\nSecond, facility accessibility serves as a proxy for the service dimension of urban built environments.\nAccessibility quantifies residents’ ease of reaching essential facilities such as education, healthcare, and\npublic services, which are critical to livability and social equity. This aligns with established accessibility-\nbased frameworks that treat spatial accessibility as an indicator of service sufficiency and urban functional\nefficiency.\nThird,infrastructuresufficiencycapturestheadequacyandbalanceofurbanamenitiesandlivingcondi-\ntions. Theselectedindicators—neighborhoodamenitydiversity,inequalityofresidencecapacityallocation,\nand per capita infrastructure occupation—jointly describe the diversity, equality, and efficiency of urban\ninfrastructuredistribution. Diversityservesasaproxyforfunctionalsufficiencyandadaptability,whilethe\nGini-basedinequalitymeasureandper-capitaoccupationreflectfairnessandper-personresourceavailabil-\nity. Together,theseindicatorsextendtheassessmentfromquantitytoqualityanddistributionalequity.\nOverall,thesethreedimensionsandnineindicatorscomprehensivelyrepresentthestructural,functional,\nanddistributivecharacteristicsofthebuiltenvironment. Theirselectionalsoreflectspracticaldataavailabil-\nityandmethodologicalconsistencyacrosscities,enablingsystematiccomparisonandscalableimplementa-\ntion. Wehavesupplementedtheexplanationanddetailsintherevisedmanuscript(Lines69–72).\nReviewer Comment2.6 — Some patterns recur across tiers, regions, and intra-city analyses. Consider\nmoving granular statistics to the Supplementary Information and keeping the main text to the distinctive\ninsightsateachscale. Thiswillimproveflowwithoutreducinganalyticaldepth.\nReply: Thank you for your constructive comments. To make the analysis of the three scales in the main\ntext more concise, we have removed and rewritten certain content based on the overall analysis pattern of\nthemanuscript.\n(1) We move several contents and discussions about facility accessibility among city tiers from Fig. 2 to\nSupplementary Fig. 4. As the original Fig. 2 demonstrated both the accessibility in 0–120 mins and 0–30\nminsinthesamefigure. Toillustratetheresultsclearly, wemovethe0–120minplottothesupplementary\nfigure.\n(2) We revised some expressions to make the analyses clearer in the revised manuscript at Lines 133–136.\nAndweaddedaparagraphinthediscussionparttosummarizeandintegrateanalysesacrossdifferentscales\nanddimensionstomaketheresultsandconclusionsclearer(Lines294–301).\n(3)Weremovesomeredundantcontentaccordingtotheanalyzedcontextintherevisedmanuscriptatlines\n130–133,148–149,217–218,281–282,and312–314.\nheadline findings. Please reconcile these by (a) mapping each main finding to a stated question, or (b)\nmodestlyrevisingthequestionssoreaderscantracethelogicfromquestions→methods→results.\nReply: Thank you so much for your constructive comments. Indeed, this is an important suggestion to\nimprovethelogicofthisstudy. Ourresearchquestionliesin(1)Whatarethenationalpatternsofbuilding\nfunction across China’s urban landscape? (2) How do disparities in the residential built environment vary\nacrosscitytiers,geographicregions,andintra-cityzones?\nThe answer to the first research question corresponds to the mapping product developed in this study.\nThis product directly reflects the building patterns across 109 Chinese cities, providing the key foundation\nfortheoverallanalysis. Asfortheanalyticalframework, ouranalyses areorganizedaccordingtothethree\nscales outlined in Research Question 2. The three main findings highlighted in the abstract correspond\nprecisely to these three scales. To clarify these connections, we have made the following revisions by\naddingtheanalysisscalesineachfinding:\n“(1) Across city tiers, accessibility and amenity diversity decline sharply from top- to low-tier cities,\nwhile mid tiers show more equitable housing allocation; (2) Geographically, southern cities exhibit the\nhighest access to healthcare, education, and public services but suffer from infrastructure overcrowding;\nand(3)Withincities,later-expandingzonesexhibitgreaterdisparitiesthanearly-establishedurbancores.”\n(Line15).\nReviewer Comment2.8 — Lines 185–191 define three intra-city areas. Because spatial delineation can\ninfluence disparity estimates, please add a short justification (citations, planning practice, or empirical cri-\nteria). If feasible, a brief sensitivity note (e.g., similar results under an alternative buffer or density-based\nboundary)wouldreinforcerobustness.\nReply: Weappreciatethereviewer’sinsightfulcomment. Intherevisedversion,wehaveprovidedaclearer\njustification for the intra-city delineation. Using Beijing as an example, we reclassified urban subareas\nbased on both population density and building density, in addition to the original administrative (policy-\nbased) boundaries. To assess sensitivity, we compared the distributions of built environment indicators\nunder the three classification schemes (Fig. R12): (a) function-based delineation, (b) population density-\nbaseddelineation,and(c)buildingdensity-baseddelineation. Althoughminorvariationsinindicatorvalues\nwere observed across different delineation criteria, the relative ordering among urban cores, intermediate\nzones, andurbanperipheriesremainedconsistent. Thisconsistencysuggeststhatourfindingsarerobustto\nalternative spatial definitions. In the revised manuscript, we supplemented such discussion and conclusion\natlines211–213.\na b c\nFigure R12: Infrastructure sufficiency across the three within-city zones in Beijing. The zones are delineated\nbasedon(a)administrativeboundariesandfunctionalroles,(b)populationdensity,and(c)buildingdensity.\nranges/variances. Please clarify the normalization applied prior to aggregation and the rationale for equal\nweights.\nReply: First,wenormalizedeachindicatorwithdifferentrangesorvariancestoa0–10scale,ensuringthat\nthescoresforallindicatorsfallwithinthesamerange. Second, whencalculatingtheoverallcityscore, we\nassignedequalweighttoeachindicatoroffacilityaccessibilityandsufficiency(i.e.,accessibilityofeduca-\ntionresources,accessibilityofhealthcareresources,accessibilityofpublicserviceresources,neighborhood\namenitydiversity,equalityinresidentialcapacityallocation,andpercapitainfrastructureoccupation).\nThis\nisbecauseeachoftheseindicatorsreflectsadistinctandessentialaspectofresidents’dailyinteractionswith\nthebuiltenvironment,andindividualneedsforthesefacilitiesvaryandaredifficulttoquantify. Therefore,\nweconsiderusingthemean(ratherthansubjectivelyfavoringoneaspect)tobemorereliable. Inmany\nstudies of urban environments, multiple indicators are also commonly treated as equally weighted[R9,R10]. The\nclarificationofthenormalizationmethodwasexplainedinFig. 5anditscaptionintherevisedmanuscript.\nReferenceofthisreply:\n[R9]Sun,L.,Chen,J.,Li,Q.andHuang,D.,2020. Dramaticunevenurbanizationoflargecitiesthroughout\ntheworldinrecentdecades. NatureCommunications,11(1),p.5366;\n[R10] Xu, Y., Olmos, L.E., Abbar, S. and Gonza´lez, M.C., 2020. Deconstructing laws of accessibility and\nfacilitydistributionincities. ScienceAdvances,6(37),p.eabb4112.\nReviewer 3\nI enjoyed reading this manuscript on mapping building functions to analyze large scale dynamics in urban\nenvironments. The argument for using fine-scale building information is well motivated. I agree that such\nresolution, in addition to the detailed information on building function, can reveal patterns that are not\nobservable at coarser scales or lower levels of detail. Overall, the manuscript is well written and in my\nviewsuitableforpublicationafterminorrevisions. Mostofmycommentspertaintofocus,conciseness,and\nplacementofcertaininterpretations.\nReply: Thank you so much for your constructive and positive comments. We agree that addressing these\ncommentsisveryimportantforimprovingthemanuscript. Belowarethepoint-to-pointresponsesforeach\nofthecomments.\nReviewer Comment3.1 — Line 38 - The manuscript currently emphasizes that the insights can be used\nto guide policy and planning. In my view, the discussion provides primarily a historical interpretation of\ntheobservedpatterns;althoughrelevant,thisisbroadandnoteasilyactionableatthepolicylevel. Isuggest\ntemperingthisclaim.\nReply: Thanksforyourcomment. Weagreewithyourviewpoint. Thisresearcharticleprimarilyprovides\nanoverviewofthecurrentsituationresultingfromhistoricalpolicies. Totempertheclaim,wehavemodified\ntheoriginaltexttotheversionbelow:\n“Our results revealed a systematic, multi-dimensional disparity spanning urbanization intensity, facil-\nity accessibility, and infrastructure sufficiency. Our analysis provides a valuable framework that situates\ntoday’s patterns in their policy legacies, contributing to the ongoing discourse on inequality in the built\nenvironment.”(Line37–40).\nprecedingparagraph. Considershorteningorremovingtoavoidredundancy.\nReply: Thank you for your suggestion. We shorten the content to “Numerous studies have sought to\nassessdisparitiesinresidentialenvironments,yetmostremainconstrainedbycoarsespatialresolutionand\none-dimensionalperspectives.”(Line55–56).\nReviewer Comment3.3 — Figure 3(b) - Consider limiting the x-axis to 0-30 for clarity, and moving the\nfull0to120versiontoanappendix.\nReply: Thank you for your constructive comments. We agree that moving 0–120 plots to the appendix\nwill makethe figure clearer. In the revised manuscript, we have modified Fig. 2 (shown in Fig. R13(b) in\nthis response letter) by only showing the 0–30 accessibility plots. Meanwhile, we have moved the 0–120\naccessibilityplotstoSupplementaryFig. 4(showninFig. R14(a)ofthisresponseletter).\nFigureR13: Builtenvironmentdisparitiesacrosscitytiers: urbanizationintensityandfacilityaccessibility. a,\nLeft: distributionofbuildingheightsacrossfivecitytiers,withthemodevaluelabeledforeachtier(asthepeakofthe\ncurve). Right: statisticalcomparisonofbuildingdensityandnighttimelightintensityamongcitytiers. b,Cumulative\naccessibilitycurves showingtraveltime fromresidentialbuildings tothe nearesteducational, healthcare, and public\nservicefacilities. c,Populationdistributionandaccessibilitymapsofrepresentativecitiesfromeachtier.\nshowingtraveltimefromresidentialbuildingstothenearesteducational,healthcare,andpublicservicefacilities. b,\nThepopulationdistribution,accessibilitymapstothreetypesoffacilities,andtheircumulativecurvesoftypicalcities\namongfivetiers.\nmentofresults. Itwouldfitbetterinthediscussion. Alternatively,revisetosuccinctlysummariseonlywhat\nisshowninthedata.\nReply: Weappreciatethereviewer’sinsightfulcomment. Theinterpretativecontenthasbeenremoved,and\nthesectionhasbeenrevisedtofocussolelyonaconcisepresentationoftheempiricalresults. Intherevised\nmanuscript,wemadetherevisionas: “ThissuggeststhatTier2cities,despitetheireconomicsignificance,\nmayfavorhorizontallyexpansivedevelopmentoververticalgrowth. Thesepatternsindicatethatdensityand\nheight—thoughoftencorrelated—mayfollowdivergenttrajectoriesdependingondevelopmentstrategyand\nplanning constraints at the tier level. Overall, the results show that Tier 1 cities have the tallest buildings,\nbutthedifferencesamongtiersremainrelativelymodest,rangingfrom13mto10m. Inthebuildingdensity,\nTier2citiesexhibitaslightlyhigherdensitythanTier1, whilebotharesubstantiallyhigherthantheother\ntiers.”(Lines130–136).\nReviewer Comment3.5 — Line 143 - ”This co-location suggests...” is another interpretive sentence in\ntheResultssection;consideradjustingorremovingtomaintainfocus.\nReply: Thankyouforyoursuggestions. Wehaveremovedandmarkeditintherevisedmanuscript: “This\nco-location suggests a spatial coupling of infrastructure provision and settlement concentration.” (Line\n148–149).\nReviewerComment3.6 — Line210-Removethefinalsentenceofthisparagraph.\nReply: Thank you for the suggestions. We have removed and marked it: “These results underscore the\nurgent need for more inclusive urban planning and infrastructure investment in emerging urban districts.”\n(Line217–218).\nReviewerComment3.7 — Line257-Replace”unprecedented,”whichistoostrongforthiscontext.\nReply: Thankyouforthesuggestions. Wecarefullycheckedthewholemanuscriptandchangeditto“fine-\ngrained”. Forexample,“Thefine-grainedbuilding-leveldetailsallowustouncoverdisparitypatternsthat\nremainhiddeninconventionalgriddedoradministrativedatasets.”(Line264).\nReviewer Comment3.8 — Line 274 - Remove the sentence beginning ”Our building-level...” as the\nfollowingsentenceconveysthesamepoint.\nReply: Thank you for the suggestions. We removed and marked it in the revised manuscript: “Our\nbuilding-level,multi-indicatorframeworkcapturesthespatialandstructuralcomplexityofdisparity,revealing\nhowdifferentdimensionsinteractintheurbansystems.”(Line281–282).\nReviewer Comment3.9 — Line 297-300 - The sentence beginning ”Despite these limitations...” is re-\ndundantwithearliertext. Considerdeleting.\nReply: Thank you for your suggestion. We have removed and marked it in the revised manuscript:\n“Despite these limitations, this study represents a substantial advance in resolving urban spatial structure\nattheindividualbuildingscaleandhighlightsthepotentialofsatellite-basedmethodsforunderstandingand\nmonitoringthebuiltenvironmentacrossrapidlydevelopingregions.”(Line312–314).\nwasrequired,andifnot,explainwhythiswasunnecessary.\nReply: Wegreatlyappreciateyoursuggestionsregardingthedetailsofdatapreprocessing. First,although\nthe three raster modalities, e.g., RGB imagery, nighttime light (NTL), and building height, are all in the\ngeographiccoordinatesystemofWGS1984,weobservedduringpreprocessingthattheNTLdataexhibited\nanoffsetofapproximatelythreepixels. Therefore,weperformedco-registrationontheNTLdatatoalignit\nwith the RGB imagery and building height data. As shown in the Fig. R15, after co-registration, our three\nmodalities are mutually aligned and consistent with the reference basemap (e.g., the basemap in ArcGIS\nPro). Furthermore,SupplementaryFig. 8(a)–(c)inthesupplementaryalsodemonstratesthefinalversionof\nthethreeinputrasters.\nIn the revised manuscript, we have added the following description: “Furthermore, all three raster\nmodalities were in the geographic coordinate system of WGS 1984. To ensure stricter spatial correspon-\ndence among them, we performed co-registration on the input raster data. Supplementary Fig. 8 (a)–(c)\nshowsthethreeprocessedinputrasters.”(Line356–359).\n[REDACTED]\nFigureR15: Demonstrationoftheco-registrationprocessinasampleregion,includingthefiguresofNTL–basemap,\nbuildingheight–NTL,andRGBimage–BTL.\nReviewerComment3.11 — Line448-Removetheredundantpoint..\nReply: Weapologizefortheoversightandhaveremovedit(Line360).\nReviewerComment3.12 — Line479-484-Theaccessibilityinformationappearstobepartoftheinput\ndatausedintheanalysis. Whyisitlistedunderevaluationdata?\nReply: We thank the reviewer for pointing this out. The accessibility information was indeed part of the\ninput data rather than the evaluation dataset. We have revised the manuscript accordingly by moving this\ncontenttotheendoftheMulti-sourcesatellitedatasection(Line361–367).\nReviewer Comment3.13 — Line 535 - Please clarify what is meant by ”unmapped buildings.” Under\nwhatcircumstanceswouldsomebuildingsbeunmapped?\nnotesbuildingpolygonsthatremainedwithoutadefinitivefunctionallabelaftertheinitialraster-basedpre-\ndictionandthesubsequentmajority-areaassignmentrule. Aftervectorizingtherasteroutput,wecomputed\nthepercentageareacoveredbyeachpredictedfunctionalclassforeachbuildingpolygon; ifoneclasscov-\neredover50%ofthepolygonarea,weassignedthatclasstotheentirefootprint. Buildingpolygonswhere\nnosingleclassexceededthis50%threshold(forexample,becausethefootprintwasonlypartiallycovered\nbypredictionsandcontainedmixedpixels)werelabeledas“unmapped.”Thesefootprintswerethenspecif-\nically targeted in a post-processing stage: a lightweight Mask R-CNN classifier—guided by bounding-box\npriors of the building footprints—was applied to infer their functions, thereby ensuring that every building\npolygonreceivedaconsistentfunctionallabelinthefinalvectorproduct. Wefurtherclarifiedtheworkflow\ninPart3ofFig. R16.\nIntherevisedmanuscript,weclarifiedthedetailsinthefollowing:\n“Buildings for which no single functional type occupied more than 50% of the footprint area were\nlabeledas“unmapped”,indicatingthattheirfunctionscouldnotbeconfidentlydeterminedduetomixedor\nincompleteclasscoverage.”(Line459–462).\n[REDACTED]\nFigure R16: Theoverallworkflowofthebuildingfunctionmappingandresidentialbuiltenvironmentassess-\nment,illustratingthedatasourcesandmainoperations.Thestatistical-levelvalidationisbasedonreportsfromthe\nNationalBureauofStatistics(NBS)andtheMinistryofHousingandUrban-RuralDevelopmentofChina(MURD).\nThebuilding-scalevalidationisbasedonthepointsetsoftheAmap,Inc.\nencoder? (e.g.,boundingboxcoordinates,rasterizedmasks?)\nReply: The building footprints were passed to the geo-prior encoder in the form of normalized bounding\nboxcoordinates. Specifically,thecoordinatesofeachboundingboxwerenormalizedrelativetothespatial\nextentofthecorrespondingimagepatch,wherethetop-leftcornercorrespondsto(0,0)andthebottom-right\ncorner corresponds to (1, 1). For example, a building occupying the central quarter of an image would be\nencoded as a rectangle with coordinates{(0.25,0.25),(0.75,0.75)}. We have also added a brief clarification\nofthisimplementationdetailtothemaintext:\n“... Each building footprint was represented by its bounding box coordinates normalized to the image\nspace,wherethetop-leftcornercorrespondsto(0,0)andthebottom-rightcornerto(1,1). Thesenormalized\ncoordinateswerefedintothegeo-priorencoder,...”(Line466–471)\nReviewerComment3.15 — Line585-Onwhatbasiswasthisthresholdselected?\nReply: Beforeweconductedtheanalysis,wecarefullyreviewedmanyexistingstudiesonurbannighttime\nlight. Wefoundthatthewidelyusedurbanboundarymapproduct(GAIA)[R11] ishighlyrelatedtoouranal-\nysisasthemapwasdevelopedandwell-validatedbasedonnighttimelightdataandmanualinterpretation.\nFig. R17(a)and(b)showtheoverlayoftheSGDSAT-1nighttimelightimageryusedinthisstudyand\ntheGAIAurbanboundaries. WefoundthattheGAIAboundariesclearlydelineatehumansettlementswith\nhigh nighttime light intensity. By calculating the normalized nighttime light intensity (0–255) within the\nGAIA-definedareas,weobtainedanaveragevalueofapproximately30. Furthermore,Fig. R17(c)presents\nthehistogramofnormalizednighttimelightintensityfortheareashowninFig. R17(a),demonstratingthat\nathresholdof30effectivelyfiltersoutmostdarkregions(areaswithoutevidentdensehumanactivity).\nIn general, based on previous research and our definition of high-intensity human activity areas, we\nadopted a threshold value of 30 in this study. We have included a more detailed description in: “For the\nnighttimelightintensity,wereferencedtheboundariesoftheGAIAproductandthestatisticsofnormalized\nnighttimelightintensity(0–255)withinthoseareas,adopting30asthethresholdtoindicatehigh-intensity\nhuman activity. Accordingly, we identified and quantified areas in the SDGSAT-1 nighttime light imagery\nwheretheintensityexceededthisthreshold.”(Line514–518)\nReferenceofthisreply:\n[R11] Li, X., Gong, P., Zhou, Y. et al. (2020). Mapping global urban boundaries from the global artificial\nimperviousarea(GAIA)data. EnvironmentalResearchLetters,15(9),094044.\n27\n[REDACTED]\nFigureR17: Demonstrationofthestreet-viewphotosfromapublicmapapplication.Foreachbuildingfunctiontype,\ntheclassnamesareonthetop,whilethePOIlocation(road)islistedbeloweachstreet-viewphoto.\nReviewerComment3.16 — Line593-Weredefaultweightsused,orweretheyadjusted? Pleasespecify.\nReply: We apologize for the lack of details. The accessibility time weights for roads and land-use types\nwerederivedbyintegratingtheweightingschemesfrompriorstudiesshowninR12andR13. Weusedtheir\ndefault values, and detailed weights are presented in Tables R3.1 and R3.2. In the revised manuscript, we\naddedtheexplanationinlines526–527,anddetailedweightvaluesinSupplementaryTables5and6.\nReferenceofthisreply:\n[R12]WeissDJ,NelsonA,GibsonHS,etal. Aglobalmapoftraveltimetocitiestoassessinequalitiesin\naccessibilityin2015[J].Nature,2018,553(7688): 333-336.\n[R13]HuS,GeY,LiuM,etal. Village-levelpovertyidentificationusingmachinelearning,high-resolution\nimages, and geospatial data[J]. International Journal of Applied Earth Observation and Geoinformation,\n2022,107: 102694.\nLandCoverType Definition TimeCost\n(min/km)\nAreascoveredwithtreestypicallyhavinglargecanopies\nForest 37\nandheightsexceeding5meters.\nShrubland Areasdominatedbyshrubsandlowwoodyvegetation. 14.2\nGrassland Areascoveredbyshortherbaceousplants. 12.3\nAreas with permanent or seasonal water accumulation\nWetland 30\nandvegetationgrowth,suchasswampsandmarshes.\nLandusedforagriculturalcultivation,includingfarmland\nCropland 24\nandcropareas.\nSnow/Ice Areascoveredbyextensivepermanentsnoworice. 37\nAreaswithsparsevegetationorexposedsurfacesofsand,\nBarrenLand 20\ngravel,orbaresoil.\nHuman-made structures and impervious homogeneous\nBuilt-upArea 6\nsurfaces.\nTableR3.2: Timecostassociatedwithdifferentroadtypes\nRoadType Definition TimeCost\n(min/km)\nNationalorprovincialexpressways,typicallymulti-lane\nMotorway 0.5\ntollroadswithlimitedaccess.\nTrunkRoad Majorarterialroadsconnectinglargecitiesorregions. 1\nPrimaryRoad Principalroadswithincitiesorconnectingmajortowns. 1\nRoadslinkingsecondaryurbancentersorservingassub-\nSecondaryRoad 1.5\nmainroadsincities.\nLocalorminorroadsconnectingresidentialareasorvil-\nTertiaryRoad 6\nlages.\nRailway Urbanorintercityrailwaytransportationnetworks. 1.5\nRiversandcanalsprimarilyusedforinlandwatertrans-\nWaterway 3\nportation.\n29"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Decision: Accept\nI have reviewed the revised manuscript and the authors' detailed response. The authors have satisfactorily addressed all the concerns raised in my previous review.\nSpecifically, the inclusion of technical details regarding the Paraformer framework, the expanded visualization to additional cities, and the commitment to open data significantly strengthen the manuscript. The clarifications on parameter thresholds and the additional analysis controlling for population distribution have resolved my doubts regarding the robustness of the results. Furthermore, the refined POI validation process improves the reliability of the dataset.\nI have no further comments and recommend the paper for publication.", "reviewer_2": "In this revised version, the authors have responded thoroughly and constructively to my earlier comments. The new discussion paragraph clarifying how results converge and diverge across tiers, regions, and intra-city areas, and how the three dimensions interrelate, substantially improves the conceptual coherence of the work. The added definition table for building functional classes, including formal definitions and typical POI proxies, will greatly enhance the clarity, interpretability, and reusability of the dataset. Methodologically, the authors now clearly explain how mixed pixel labels within buildings are reconciled and how multimodal inputs are normalized, which strengthens transparency and reproducibility. The inclusion of a compact visualization of data acquisition times, alongside an assessment of land-cover dynamics, appropriately addresses concerns about temporal heterogeneity in the inputs.\nAt the analytical and framing levels, the manuscript is also considerably improved. The rationale for the three dimensions and nine indicators is now better grounded in established practice, and the reconciliation between the stated research questions and the main findings makes the narrative more logically traceable. The streamlining of results between the main text and Supplementary Information has improved readability without sacrificing analytical depth. In addition, the justification and sensitivity check for intra-city delineations, as well as the clarification of normalization and equal weighting when aggregating indicators, address my remaining robustness concerns.\nOverall, the authors have satisfactorily addressed all of my previous comments, and I have no further substantive suggestions. I believe the manuscript now meets the standards for publication in Nature Communications, and I look forward to seeing it published.", "reviewer_3": "Dear authors,\nThank you for the thorough and thoughtful responses to the questions raised by me and the other reviewers. I am satisfied with your clarifications and with the revisions made to the manuscript. As mentioned previously, I consider both the final product and the proposed methodology to be valuable contributions to the field of large-scale urban remote sensing and disparity studies. In my view, the article has now reached a publishable standard.\nRegarding my review of the code repository, I have not executed the code, but I examined the structure and documentation. The GitHub repository and the accompanying Python scripts are logically organized and well documented, which should facilitate both reproducibility and application to other study areas."}, "author_response": ""}]

Decision letter