Building height is a pivotal factor in studying urban form and understanding the impacts of the vertical characteristics of urban areas on the environment. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) employ photon-counting LiDAR to collect Earth’s surface elevation data globally. However, method at ground and building photon classification and subsequently building height extraction from photon-counting LiDAR data is still missing. This study aimed to develop a methodological framework to retrieve building height from ICESat-2 data under various data acquisition conditions stratified by day/night condition, strong/weak beam. First, the noise removal algorithm adopted by ATL03 product is utilized to filter out noise photons and retain signal photons in the raw dataset. Second, the random sample consensus (RANSAC) algorithm and statistical method are adopted to classify the signal photons into ground and building photons according to their characteristics and spatial neighborhood relationship. The building height is then computed by the elevation of building and ground photons. Finally, estimated heights are evaluated using the reference building height derived from terrestrial laser scanning (TLS) data. The results indicate that the proposed methodological framework can effectively identify building and ground photons under different building types. Strong consistence between estimated and reference building height is obtained by quantifying the estimation precision of building height under different data acquisition conditions, root mean square error (RMSE) ranging between 0.35 m and 0.45 m, indicating that the ATL03 data can be utilized to derive the building height in urban areas for all acquisition times and beam intensity. Further analysis demonstrates that strong/weak beam significantly influences the building height estimation compared to day/night condition. Overall, this study provides a method for estimating building height in urban areas using ICESat-2 data, and the findings approve the strong capability of ICESat-2 data in estimating building height.

建筑高度是研究城市形态和理解城市地区垂直特征对环境影响的关键因素。冰、云和陆地高程卫星 2 (ICESat-2) 使用光子计数 LiDAR 在全球范围内收集地球表面高程数据。然而，地面和建筑物光子分类以及随后从光子计数 LiDAR 数据中提取建筑物高度的方法仍然缺失。本研究旨在开发一种方法框架，以在按日/夜条件、强/弱光束分层的各种数据采集条件下，从 ICESat-2 数据中检索建筑物高度。首先，利用ATL03产品采用的去噪算法过滤掉噪声光子，保留原始数据集中的信号光子。第二，采用随机样本一致性（RANSAC）算法和统计方法，根据信号光子的特性和空间邻域关系，将信号光子分为地面光子和建筑光子。然后通过建筑物和地面光子的高程计算建筑物高度。最后，使用源自地面激光扫描 (TLS) 数据的参考建筑物高度评估估计高度。结果表明，所提出的方法框架可以有效识别不同建筑类型下的建筑和地面光子。通过量化不同数据采集条件下的建筑高度估计精度，均方根误差（RMSE）范围在0.35 m到0.45 m之间，获得估计和参考建筑高度之间的强一致性，表明 ATL03 数据可用于在所有采集时间和光束强度下推导出城市地区的建筑物高度。进一步分析表明，与白天/夜晚条件相比，强/弱光束显着影响建筑物高度估计。总体而言，本研究提供了一种利用ICESat-2数据估算城市地区建筑高度的方法，研究结果证实了ICESat-2数据在估算建筑高度方面的强大能力。