发表机构
Université Gustave Eiffel（古斯塔夫·埃菲尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合TerraSAR-X StripMap、PlanetScope与Sentinel-1数据，采用地理加权随机森林模型，在巴西某大城市实现建筑高度估计，RMSE为5.34m、R²为0.756，揭示了不同建筑类型的特征重要性差异，为相关应用提供了选择指导。
AI 中文摘要
在单个建筑 footprint 尺度下获取准确的建筑高度信息，对材料存量核算和灾后损害评估至关重要，但在全球南方地区，由于机载LiDAR覆盖稀少，且商业超高分辨率图像成本高昂或无法获取，城市尺度的建筑高度信息仍难以获得。尽管近期研究已展示了利用免费Sentinel图像进行建筑高度估计的方法，但所得产品的分辨率上限对于材料存量分析而言仍较为粗糙。本研究将来自科研许可下可免费获取的数据产品（TerraSAR-X StripMap和PlanetScope）与Sentinel-1相结合，用于预测巴西某大城市的建筑高度。为解决训练集中的空间自相关性问题，所有来源的特征被整合到地理加权随机森林模型中，与LiDAR参考数据集相比，该模型的均方根误差（RMSE）为5.34米，决定系数（R²）为0.756。局部特征重要性分析显示，预测变量的主导地位在城市内部不同区域存在一致变化：低层建筑的主导特征是 footprint 几何形状，较高且更孤立的建筑的主导特征是阴影衍生高度，该区域最高建筑的主导特征是光谱反射率；Sentinel-1后向散射和InSAR占据互补的空间生态位，没有单一传感器在所有场景中都更优。研究结果提供了卫星衍生产品在不同场景下预测相关性的选择指导和见解，这是全局机器学习或神经网络模型无法提供的。
英文摘要
Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South where airborne LiDAR coverage is rare and commercial very high-resolution imagery is cost-prohibitive or unavailable. While recent works have demonstrated building height estimation using freely available Sentinel imagery, the resolution ceiling of resulting products is still coarse for material stock analysis. This study incorporates products derived from data freely accessible under scientific research licenses, TerraSAR-X StripMap and PlanetScope, alongside Sentinel-1 to predict building heights in a large city in Brazil. To account for the spatial autocorrelation in the training set, features from all sources are integrated in a geographically weighted random forest model, returning an RMSE of 5.34 m and R2 of 0.756 against a LiDAR reference dataset. Local feature importance showed predictor dominance to vary consistently across intra-urban contexts, with footprint geometry dominating for low-rise buildings, shadow-derived height for taller and more isolated structures, and spectral reflectance for the tallest buildings in the set. Sentinel-1 backscatter and InSAR occupy complementary spatial niches, with no single sensor uniformly preferable across the set. Results provide optioneering guidance and insight over satellite-derived products predictive relevance in distinct contexts, which global machine learning or neural network models cannot offer.
Comments4 pages, 3 figures, intended for JURSE 2027
