1. Introduction
Knowing building height is essential for understanding urban
process regimes, enabling improved urban management and
planning. Despite advancements in global scale datasets, such
as those developed by (Kamath et al., 2024), comprehensive
building-height information for Japanese cities remains notably
absent. Most existing studies rely heavily on imagery-based
methods, including satellite imagery, street view images or Syn-
thetic Aperture Radar (SAR) data derived from Sentinel-1 and
Sentinel-2 time series. (Frantz et al., 2021). For instance,
(Yang and Zhao, 2022) employed spatially-informed Gaussian
Process Regression with Sentinel-1 data for major Chinese cit-
ies, while (Cai et al., 2024) utilized building footprints com-
bined with a self-adaptive buffer for building photon selection
methods to improve height estimations.
To address these gaps, we propose a novel building-height pre-
diction method specifically tailored to Japanese urban contexts.
In contrast to previous studies that primarily measure height for
existing buildings, our objective extends to provide building-
height estimations necessary for generating hypothetical urban
scenarios within urban digital twins. This functionality is es-
sential for simulation urban development processes and evalu-
ating planning policies through digital twin applications. For
the first time, our approach integrates urban planning inform-
ation, explicitly considering building footprints, local zoning
regulations, and detailed roof-type attributes derived directly
from building geometry. Incorporating roof-type features pre-
dicted from footprints, alongside traditional urban morpholo-
gical characteristics, our method significantly enhances predict-
ive accuracy. The main contributions of our research are: it
fills the critical data gap in global building-height datasets, par-
ticularly addressing the absence of detailed Japanese data; and
it provides a robust, high-resolution building-height prediction
model directly applicable to digital twin frameworks, facilitat-
ing the generation of realistic 3D urban models.
2. Methods
2.1 Data
This study utilizes the PLATEAU dataset, a detailed 3D urban
model produced from aerial surveys and other geospatial meas-
urements, developed and maintained by the Ministry of Land,
Infrastructure, Transport and Tourism (MLIT) in Japan. As
of August 2024, when our dataset was acquired, PLATEAU
provides publicly accessible 3D urban model data covering
212 municipalities in Japan. We employed Plateaukit (Ozeki,
2024), an open-source tool designed specifically for access-
ing and processing PLATEAU data are available in GeoJSON,
CityJSON, and Parquet formats; in this research, we utilize the
Parquet format. Both our target variable—measured building
height—and urban morphological features are sourced directly
from this dataset.
Urban planning information incorporated in our study primarily
refers to the ”Use Districts” (Japanese: youto chiiki), a founda-
tional zoning regulation in Japan. These districts regulate build-
ing usage and parameters such as Floor Area Ratio (FAR) and
Building Coverage Ratio (BCR), and are categorized into 12
distinct types covering residential, commercial, and industrial
purposes (Ma et al., 2024). Given that zoning regulations sig-
nificantly influence building height and form in Japanese cities,
we regard them as crucial inputs for urban digital twin applic-
ations that simulate urban planning scenarios, and thus involve
them in our model. We sourced this zoning regulation data from
the MLIT Urban Planning Information Dataset (Toshi-keikaku
Kettei Joho), which provides nationwide coverage. We found
that zoning information for 98 municipalities was already in-
tegrated into the PLATEAU dataset, enabling direct usage for
our analysis.
In addition, road network data were included to compute two
urban morphological characteristics—level and width of the ad-
jacent road—as features in our model. These road network data
were provided by the Japan Digital Road Map Association.
Finally, we introduced roof type data generated through a roof
classification algorithm developed by our research group. This
model integrates satellite imagery with building footprint data
to classify roof structures into several typical roof categories,
achieving classification accuracy exceeding 95%. Roof type in-
formation is uncommon in most building datasets; hence, its
inclusion provides valuable architectural insights that enhance
the accuracy of our predictive model.
For data cleaning, we selected cities that simultaneously in-
cluded complete datasets mentioned above. Buildings with
measured heights below 3 meters (one floor) or extremely large
outlier values, as well as any observations with missing values,
were removed to ensure data integrity and predictive reliability.
2.2 Feature Engineering
Referring to existing building-height prediction mod-
els (Milojevic-Dupont et al., 2020, Stipek et al., 2024),
and incorporating knowledge from urban science, we selected
a total of 13 predictive features (Table 1). These include
building geometric characteristics, neighborhood context, and
regulatory information.
2.3 Machine Learning Methods
To enhance the predictive effectiveness of our machine learn-
ing models, we first preprocessed the zoning regulation data.
Given the considerable diversity and overlap in building heights
across the numerous categories of zoning regulations (”Use
Districts”), we applied K-means clustering to regroup these
zoning categories based on their height characteristics. Spe-
cifically, we computed statistical measures for each original
”Use District” category—mean, standard deviation, maximum,
minimum, median, and skewness of building heights—as clus-
tering features. This clustering process resulted in three distinct
zoning groups, each modeled separately to ensure greater ac-
curacy.
Supervised learning methods have been widely applied in
building-height prediction tasks (Milojevic-Dupont et al.,
2020). In line with previous research, our study employed su-
pervised machine learning algorithms utilizing publicly avail-
able building and urban planning data. We compared three ma-
chine learning algorithms: Random Forest Regression (RFR),
Support Vector Regression (SVR), and Extreme Gradient
Boosting (XGBoost).
We conducted model training and evaluation using a dataset
comprising 15,036 buildings from five selected Japanese cit-
ies: Sendai, Maebashi, Omuta, Chino, and Tokushima. The
dataset was split into a training set (70%) and a test set (30%).
Considering that a substantial proportion of buildings were be-
low 10 meters in height, we implemented stratified sampling
within each zoning group to balance the height distribution in
the training dataset, thus ensuring model robustness across the
entire height spectrum. All models were trained using cross-
validation techniques, with the training subset from the afore-
mentioned cities, to validate the accuracy and generalizability
of the height predictions on the testing subset.
3. Results
The predictive performance of the three supervised learning al-
gorithms is presented in Table ??. The models were evaluated
using Mean Absolute Percentage Error (MAPE), Mean Abso-
lute Error (MAE), and the coefficient of determination (R2).
Overall, RFR achieved the best predictive performance among
the three models, particularly in terms of the coefficient of
determination (R2) and mean absolute error (MAE). Notably,
RFR obtained the highest R2 value of 0.67 in Group 2, which
corresponds to the zoning category with the most consistent
height distribution. This suggests that the model captures the
relationship between building features and height most effect-
ively in more regulated or homogeneous areas.
SVR showed relatively high MAE and unstable R2 values, es-
pecially in Group 1, indicating poor generalization for more di-
verse urban forms. Although the full-dataset performance of
SVR was comparable to RFR in terms of MAPE, its predictive
reliability was notably lower.
XGBoost yielded consistent results across groups but underper-
formed compared to RFR. Its relatively lower R2 values and
higher errors suggest that while robust.
