This study employs three basic types of land-use conversion scenarios established in previous literature (Huang et al., 2020b; Stilla and Xu, 2023; Zhao et al., 2023), to generate annual reference building heights that account for urban renewal. The scenarios are defined as follows:

New construction: areas transition from non-building land cover (e.g., water, grassland, barren land) to built-up/impervious land, characterized by the emergence of new buildings and an increase in height from zero.

New construction areas are identified using the GAIA and WSF 2019 settlement datasets. Complete demolition areas are inherently absent from the 2019 reference data. However, GAIA and WSF 2019 cannot capture demolition process, as they only identify new impervious surfaces and neglect conversions from building to non-building. To address this limitation, the CCDC is applied to identify persistent built-up areas based on the 2019 reference data. These areas are then used to derive annual reference building heights (Huang et al., 2020a).

The CCDC mask records time of the last detected breakpoint. For a given year, only pixels with no breakpoint within the time period between this year and 2019 are kept as the reference data for this year. The absence of a breakpoint implies a high probability that these pixels experience no land-use transitions, therefore buildings within are neither newly constructed nor demolished, and thus retain the building height from the 2019 reference data. Given a target historical year t (1990≤t≤2018) and pixel (x,y), the annual reference height Ht(x,y) is determined by: 7Htx,y=H2019x,yifTbreakx,y=0orTbreakx,y<tExcludedotherwise where Tbreak(x,y) is the latest breakpoint year detected by the CCDC algorithm for that pixel, with 0 meaning no breakpoint detected. This method guarantees that the 2019 reference height is only projected backward to year t if the land surface has remained spectrally stable and undisturbed since year t. If any spectral breakpoint is detected within the window [t,2019], the pixel is completely excluded from the training-validation pool for year t.

Although CCDC data cover the period from 1999 to 2019, China's urbanization before 1999 was predominantly new district development rather than systematic urban renewal (Wu, 2016). Consequently, the 1999 CCDC masks are uniformly applied to 1990–1999, which may introduce minor yet non-negligible uncertainty to the pre-2000 period, keeping the overall accuracy around 65 %. Chen et al. (2025) adapted a similar approach of extracting long-term building height labels using CCDC dataset, with labels derived from 2020 to 2005, 2010 and 2015 achieving accuracy of over 85 %, granting the reliability of applying CCDC to obtain annual reference data.

Radar backscatters are widely utilized to develop building height datasets since they often bear valuable height information (Li et al., 2020a; Yadav et al., 2025; Wu et al., 2023; Frantz et al., 2021). However, the access of long-term high-resolution open-source radar data is of great limitation due to security reasons. Sentinel-1, as the main source of radar at present, only covers the time period since 3 October 2014.

To acquire long-term radar data, the Random Forest regression framework is adapted to fit 1 km-resolution Sentinel-1 VV backscatter with Landsat spectral bands and terrain information (Yuan et al., 2024). It achieved satisfactory performance (RMSE = 1.5 dB) in the Beijing-Tianjin-Hebei region.

The regression framework is further extended nationwide in this study, and the regressed VV backscatter is utilized as an independent variable in subsequent XGBoost-based models for building height mapping. The nationwide robustness of the 1 km reconstructed VV backscatter is confirmed through validation across diverse geographic regions characterized by different urban forms, local climates, and topographic conditions. Detailed accuracy metrics are provided in Table S6 in the Supplement. The predictor matrix includes six primary spectral bands (Blue, Green, Red, NIR, SWIR1, SWIR2), four indices (NDVI, EVI, MNDWI, NDBI), DEM, and DEM-derived slope. The reconstruction process is illustrated in Fig. S4 in the Supplement.

Annual building height mapping results show consistency with reference building heights across the research time period. From 1990 to 2019, the RMSE of XGBoost-based models (XGB-ReVV, XGB-S1 and XGB-S1S2) ranges from 3.20 to 4.27 m, while the MAE varies between 2.27 and 2.95 m (Fig. 6a). Accuracy indices exist a temporal progression: moving backward from 2019, both RMSE and MAE first increase with fluctuations, peak in 2011, then decrease until 2000, and finally relatively stabilize between 1990–2000. The error increase likely comes from limited availability of high-resolution remote sensing data sources in earlier years. The subsequent decrease and stabilization may reflect the diminishing annual reference data quantities due to CCDC masking, and decreasing proportions of high-rise buildings that cause higher estimating variations (Chen et al., 2025). The R2 fluctuates around 0.80 from 1990 to 2019 with a maximum amplitude of 0.04 (Fig. 6b), implying stable capacity of the models to explain height variance using remote sensing predictors as temporal distance increases. Overall speaking, aside from the temporal tendency, the observed temporal variability in model accuracy remains constrained within modest thresholds. The peak-to-trough ratios measure 89.5 % for RMSE, 91.3 % for MAE and 84.7 % for R2. All metrics demonstrate sustained relative stability across the three-decade. The detailed RMSE, MAE, and R2 values for each year are listed in Table S3.

The accuracy of the three XGBoost-based models is further evaluated for three representative years: XGB-ReVV in 2014, XGB-S1 in 2017, and XGB-S1S2 in 2019 (Fig. 7). These years correspond to the initial years of XGB-ReVV, XGB-S1, and XGB-S1S2, with each model employing a distinct set of predictor variables. Since each 30 m grid represents the area-weighted height of buildings and surrounding built-up surfaces, height values greater than 0 m but lower than 3 m may occur when buildings occupy only a limited proportion of the grid area, rather than indicating the absence of buildings. Accordingly, for visualization purposes only, the starting points of both the x- and y-axes are set to 0 m in Fig. 7.

The prediction accuracy remains relatively consistent across these years, with RMSE values of 3.90, 4.14, and 4.11 m; MAE values of 2.70, 2.86, and 2.83 m; R2 values of 0.80, 0.78, and 0.78; and rRMSE values of 0.61, 0.63 and 0.63, respectively (Fig. 7a–c). Linear regression slopes between reference and estimated heights are 0.77, 0.75, and 0.75 with intercepts of 0.64, 0.71, and 0.71 m. The minimal intercepts indicate negligible baseline offsets for low-rise structures. The slopes indicate a systematic underestimation of high-rise buildings, with predicted heights reaching only approximately half of the actual values. This limitation has been highlighted in several previous studies (Frantz et al., 2021; Che et al., 2024; Cao and Weng, 2024). Notably, the higher sample density in the low-height range of the density plot is expected, mainly due to the area-weighted definition of grid-level height, the dominance of low-rise buildings and sparsely built-up areas in the reference samples, and the visualization mechanism of the density scatter plot. This pattern does not indicate that the model is limited to predicting low-height buildings, nor does it imply that mid- or high-rise building samples are excluded from the validation. The height-stratified analysis of prediction errors shows that the models with ratio-controlled resampling achieve high accuracy for buildings exceeding 30 m, whereas structures below 30 m exhibit relatively lower accuracy (Fig. 7d–f). The results from linear regression and height-stratified analysis for each year from 1990 to 2019 are presented in Figs. S1 and S2.

Significant regional discrepancies in prediction accuracy are observed across the 59 training cities using the 2019 model. A distinct north-south performance difference emerges (Fig. 8). Northern cities such as Beijing (RMSE = 3.82 m, MAE = 2.71 m, R2 = 0.75) and Lanzhou (RMSE = 3.48 m, MAE = 2.50 m, R2 = 0.66) show better performances than average metrics, while southern counterparts have both out-performing cities like Yangzhou (RMSE = 2.93 m, MAE = 2.15 m, R2 = 0.33), and significantly underperforming cities like Xiamen (RMSE = 5.24 m, MAE = 2.98 m, R2 = 0.38). Cities in the Pearl River Delta exhibit particularly low accuracy, like Guangzhou (RMSE = 12.51 m, MAE = 7.67 m, R2 = -1.68), and Foshan (RMSE = 12.31 m, MAE = 7.73 m, R2 = -1.96). These regional disparities may be attributed to meteorological factors – frequent cloud cover and precipitation in southern China likely degraded optical remote sensing data quality, thereby introducing noise into the prediction models (Zhang and Weng, 2016). Accuracy assessments for each training-sample city for every year from 1990 to 2019 are recorded in Table S4.

To further evaluate the spatial transferability and cross-city robustness of the models, an independent validation is conducted using sparse building height information collected from the Lianjia real-estate platform (Fig. 9). After being matched and processed with CMAB building footprints (Zhang et al., 2025a), the Lianjia data provide community-level building heights. Since these data are neither pixel-level nor footprint-level ground truth, they are not used for model training. Instead, they provide an independent benchmark for evaluating model performance in extrapolated cities that are excluded from the reference samples.

The extrapolation validation is conducted using the XGB-S1S2 model trained with the 2019 reference samples, because this validation only requires a single-year cross-sectional assessment. The results show that this model maintains reasonable estimation performance in cities excluded from the reference samples, with an overall RMSE of 13.40 m. For representative cities, the RMSE values are mostly within 3–6 m in low-rise built-up areas, including Linyi, Puyang, Weifang, and Quzhou. For mid- and high-rise areas, the RMSE values range from 10 to 17 m in Jiangmen, Zhenjiang, Yancheng, and Xuchang.

The relatively larger errors for taller buildings are consistent with patterns reported in existing large-scale building height studies. Che et al. (2024) show that high-rise buildings tend to contribute more strongly to regional errors; for example, in Africa, the RMSE for buildings above 50 m reaches 25.52 m, which is much higher than that for buildings below 20 m. Therefore, although underestimation still occurs in some high-rise areas, the extrapolation results remain comparable to existing findings and demonstrate this model's cross-city applicability and generalization capability.

For comparison with single-year building height datasets, Beijing in 2019 is selected as the study area because of its complex urban morphology and heterogeneous building height distribution. Wu's CNBH-10 m (Wu et al., 2023), WSF 3D (Esch et al., 2022) and Ma's (Ma et al., 2024a) single-year datasets are quantified as benchmarks (Table 3).

To ensure a fair assessment across products with different height definitions, the original building height products are kept unchanged, and definition-matched reference data are generated from the same Baidu building height vectors. The vectors are first converted into a 1 m height raster and then aggregated according to each product's resolution, spatial reference, and height definition. For CNBH-10 m and Ma's dataset, only building-covered pixels are averaged and aggregated to 10 and 150 m, respectively; for WSF 3D, all pixels within each 90 m grid are averaged to match its area-weighted, volume-derived height definition. Therefore, each product is evaluated against its matched reference data using consistent accuracy metrics, rather than by directly comparing absolute height values across products with different definitions. Notably, all accuracy metrics are calculated using the full validation dataset; the axis limit of 40 or 45 m in Fig. 11 is used only for visualization to better show the dominant data distribution.

Our dataset achieves the best overall accuracy in Beijing in 2019 (Fig. 11b), with the lowest RMSE (4.58 m), rRMSE (0.48), and MAE (3.13 m), as well as a relatively high R2 (0.77). CNBH-10 m, WSF 3D and Ma's datasets expose visible biases, with RMSE differing from 6.50 to 14.39 m and MAE differing from 4.43 to 9.71 m (Fig. 11c–e). Our dataset holds less underestimation compared to other datasets, while all these datasets express a tendency to underestimate the height of high-rise buildings. The annual distribution of building heights from 1990 to 2019 is shown in Fig. S3.

Visual comparisons across four major Chinese cities, namely Beijing, Shanghai, Chengdu, and Guangzhou, show that our dataset closely approximates the reference height distributions in all cities (Fig. 12). The Wu's and Ma's datasets systematically overestimate vertical structures in Beijing, Shanghai, and Chengdu, where building heights exhibit significant variability. In contrast, Guangzhou's predominantly high-rise urban morphology shows better alignment with Wu's and Ma's estimates, while WSF 3D underestimates heights. Our dataset successfully resolves this regional discrepancy, accurately capturing Guangzhou's high-density vertical urban growth alongside other cities' height heterogeneities.

To provide a comprehensive comparison with existing long-term building height products, three representative datasets are selected (Table 4): the 1985–2010 dataset of He et al. (2023), the 2005/2010/2015/2020 dataset of Chen et al. (2025), and the China Multi-Attribute Building (CMAB) dataset (Zhang et al., 2025a). Since He's dataset and CMAB record building construction completion years, the average building height for each reference year is calculated using buildings constructed before that year. For our dataset and Chen et al. (2025), direct annual height averaging is applied.

At the nationwide scale, Fig. 13 compares annual mean height trends across the four datasets. Median values, Q1/Q3 quartiles, and 1.5× standard deviation ranges comprehensively characterize height distributions. During 1990–2019, our dataset and He et al. (2023) show similar nationwide mean heights of approximately 5.17 and 5.18 m, respectively, whereas Chen et al. (2025) reports a higher mean height of approximately 10.44 m, and CMAB exceeds 14 m. These differences are mainly related to sample coverage and reference data sources. For example, CMAB focuses more on urban center built-up areas, while the other long-term datasets cover broader regions, including rural areas, townships, and low-density built-up areas. Despite these systematic height differences, all datasets show relatively stable temporal trends at the nationwide scale, suggesting that our dataset reasonably reflects the long-term stability of building height changes in China. The detailed value of annual mean height for our dataset is recorded in Table S5.

At the urban local scale, Beijing and Guangzhou are selected for detailed comparison. In Zhongguancun, Beijing, obvious urban renewal and demolition–reconstruction occur around 2005. He et al. (2023) does not sufficiently reflect the local redevelopment process, while Chen et al. (2025) shows temporal drifting with unreasonable height fluctuations for some stable buildings. Our dataset better reflects local building height growth and renewal (Fig. 14a). In Liwan District, Guangzhou, which has remained generally stable since 2005, Chen et al. (2025) also shows temporal drifting, whereas our dataset maintains better height continuity and temporal consistency (Fig. 14b).

To evaluate the temporal stability of the generated building height dataset, stable areas are identified where building footprints and heights remain unchanged throughout the study period. These areas are defined as pixels with no change year detected by CCDC from 1990 to 2019. As shown in Fig. 15, the estimated mean building heights in these stable areas remain highly consistent over time. The highest mean height is 7.13 m in 2011, while the lowest is 6.95 m in 1992, corresponding to a fluctuation of less than 0.2 m. The mean reference height in 1990 is 5.58 m, shown by the red dotted line, and is close to the estimated heights. The orange bars show annual differences from the 1990 estimated height, with a maximum deviation of 0.17 m in 2011. These results indicate that the dataset does not introduce artificial temporal noise and maintains high temporal stability in areas without physical urban change.

To validate the reliability of the long-term building height dataset in building change areas, Figs. 16 and 17 examine three typical urban change contexts including persistent built-up, complete demolition, and new construction areas. For sampled cities, predicted heights are compared with reference heights and historical satellite imagery; for non-sampled cities, where reference height data are unavailable, historical satellite imagery is used as visual evidence for the extrapolated results.

In sampled cities, the predicted heights generally agree well with both reference heights and historical imagery. For persistent built-up areas and new construction areas, such as Drum Tower in Tianjin (Fig. 16a) and Yijiangyuan in Wuhan (Fig. 16c), the dataset effectively captures building height dynamics. Taking Yijiangyuan as an example, the reference heights in 2000, 2005, 2013, and 2019 are 5.19, 5.26, 5.69, and 6.00 m, respectively, while the corresponding predicted heights are 5.15, 5.30, 5.72, and 6.11 m, with errors all below 0.11 m. For the complete demolition case, Jiaohuachang in Beijing (Fig. 16b) is selected. The estimated height decreases from 5.71 to 4.04 m, consistent with the demolition and redevelopment process visible in historical imagery. As described in Sect. 3.1.1, annual reference heights are derived from persistent built-up areas identified using the 2019 reference data and CCDC breakpoint information, rather than direct observations of all historical building pixels. Therefore, the 2005 difference between the predicted and reference heights, 5.71 m versus 3.52 m, mainly arises from the applicability limitation of the annual reference data generation method in demolition areas. This difference reflects the limited representativeness of the reference height for demolished buildings, rather than model bias or a data error in the generated dataset.

In non-sampled cities, mapped temporal trends are broadly consistent with visually identifiable urban changes. The estimated height at Handan Congtai Park (Fig. 17a) increases from 5.90 to 7.40, 8.04, and 8.68 m, reflecting redevelopment from bungalows to multi-story apartments. At Jining Shiliying Village (Fig. 17b), the estimated height decreases from 4.05 to 3.91, 3.74, and 3.53 m, consistent with settlement abandonment and demolition caused by subsidence. For Anyang Yiyuan (Fig. 17c), the estimated height increases from 4.91 to 5.60, 7.22, and 8.63 m, reflecting progressive new construction on previously undeveloped land. These results indicate that the generated dataset can reasonably characterize long-term building height dynamics across persistent built-up, demolition, and new construction areas.

As the start and end years of our dataset, 1990 and 2019 are selected to illustrate China's building height distributions (Fig. 18a, b). Given the large data volume, visualizations are aggregated to 1 km resolution for national-scale representation. During this period, urban built-up areas expanded substantially, accompanied by a clear increase in building heights. This trend is most pronounced on the North China Plain and in fast-growing metropolitan regions of central and eastern China.

City-level temporal visualizations for Shanghai, Beijing and Shenzhen (Fig. 18c–e) depict their urbanization trajectories in 1990, 2000, 2010, and 2019. Core urban areas maintain relatively stable building heights, with only incremental additions of high-rise structures in all three cities. Peripheral districts undergo rapid land conversion and construction, resulting in taller and denser building clusters over time. For example, in Shenzhen, Luohu District had the tallest skyline in 1990, while Futian District experienced concentrated development after 2000, eventually surpassing Luohu in building height and density.

Our dataset presents a consistent depiction of both horizontal and vertical expansion of the built environment across China from 1990 to 2019, accurately capturing nationwide urban building growth over the past three decades. During this period, total building volume increased markedly from 365.98 km³ in 1990 to 882.43 km³ in 2019, while total impervious surface area expanded from 54 407.03 to 170 595.06 km².

Figure 19a and b illustrate widespread volumetric growth across almost all provinces, despite the pronounced regional heterogeneity. By 2019, national building volume reaches approximately 882.43 km³, which is generally consistent with GlobalBuildingAtlas estimates (approximately 700 km³; Zhu et al., 2025). At the city scale, our estimates display strong agreement with 3D-GloBFP (Che et al., 2024), such as Beijing (ours: 17.0 km³ vs. 13 km³) and Shanghai (ours: 12.7 km³ vs. 14.1 km³).

Figure 19c and d further show shifting contributions to national building volume. In 1990, Hebei (11.3 %), Shandong (10.6 %), and Heilongjiang (10.2 %) were the largest contributors. By 2019, Shandong becomes the highest (9.8 %), while coastal provinces such as Guangdong (9.4 %) and Jiangsu (7.7 %) gain substantially in their share, indicating accelerated coastal urbanization driven by economic growth.

Figure 19e–g show the temporal evolution of total building volume, impervious surface area, and mean building height, respectively. Although the mean building height exhibits a slight declining trend after 1990, indicating that outward expansion slightly outpaces vertical increase, both building volume and impervious surface area continue to increase. The growth rate of building volume begins to slow around 2016 and approaches a relatively stable level by 2019. For impervious surface area, a decline in growth rate occurs after 2016, with subsequent increases remaining relatively modest. Annual values for these indicators are provided in Table S5.