基于卫星激光测高和多源光学遥感合作计算大规模城市建筑高度的动态反演方法

Land Pub Date : 2024-07-24 DOI:10.3390/land13081120
Haobin Xia, Jianjun Wu, Jiaqi Yao, Nan Xu, Xiaoming Gao, Yubin Liang, Jianhua Yang, Jianhang Zhang, Liang Gao, Weiqi Jin, Bowen Ni
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引用次数: 0

摘要

建筑高度是研究城市环境和人类活动的重要指标,需要精确、大规模和精细的计算。然而,基于机器学习的推断建筑物高度的主流方法面临诸多挑战,包括样本数据有限和更新频率缓慢。另外,卫星激光测高技术为高精度计算建筑物高度提供了可靠的方法。在此,我们基于建筑屋顶等高线矢量数据集和 ICESat-2 ATL03 光子数据,初步计算了 2019 年至 2022 年沿卫星轨道的建筑高度。通过整合多源被动遥感观测数据,我们将推断出的建筑高度结果作为参考数据来训练随机森林模型,对 10 米尺度的建筑高度进行回归。与地面测量高度相比,利用 ICESat-2 光子数据构建的建筑高度样本优于利用建筑总层数间接推断建筑高度的方法。此外,模拟的建筑高度与单个城市范围内的实际观测数据密切相关。最后,利用几年的推断结果,我们分析了天津 2019 年至 2022 年的建筑高度变化。结合随机森林模型,所提出的模型可以实现大规模、高精度、频繁更新的建筑高度推断,对城市三维特征的全球动态观测具有重要意义。
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Dynamic Inversion Method of Calculating Large-Scale Urban Building Height Based on Cooperative Satellite Laser Altimetry and Multi-Source Optical Remote Sensing
Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry technology offers a reliable means of calculating building heights with high precision. Here, we initially calculated building heights along satellite orbits based on building-rooftop contour vector datasets and ICESat-2 ATL03 photon data from 2019 to 2022. By integrating multi-source passive remote sensing observation data, we used the inferred building height results as reference data to train a random forest model, regressing building heights at a 10 m scale. Compared with ground-measured heights, building height samples constructed from ICESat-2 photon data outperformed methods that indirectly infer building heights using total building floor number. Moreover, the simulated building heights strongly correlated with actual observations at a single-city scale. Finally, using several years of inferred results, we analyzed building height changes in Tianjin from 2019 to 2022. Combined with the random forest model, the proposed model enables large-scale, high-precision inference of building heights with frequent updates, which has significant implications for global dynamic observation of urban three-dimensional features.
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