使用激光雷达衍生的地形智能进行卫星地面站选址的可解释地理空间人工智能

arXiv:2607.14127v1 Announce Type: new Abstract: Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of local obstructions that drive terminal clutter loss. Current practice often relies on fixed clutter heights assigned to land use classes in Recommendation ITU-R P.452-18, but this misses within class variation and can lead to conservative exclusion zones and poor site ranking for low Earth orbit ground station siting and spectrum coordination.我们提出了一个可解释的、可在全球部署的机器学习框架，用于从开放地理空间数据预测 RCH。 The model is trained using LiDAR derived labels from the U.S. Geological Survey 3D Elevation Program and inference time features from global land-cover, terrain, demographic, thermal, and optical remote sensing products. We define RCH using a robust 75th percentile clutter height statistic, evaluate multiple regressors, and select LightGBM for its accuracy, efficiency, and compatibility with feature attribution analysis.最终模型的平均绝对误差为 1.79m，R^2=0.765，相对于 ITU 基线，绝对误差降低了 60% 以上。 Beyond aggregate fit, we evaluate domain facing criteria relevant to RF planning, including meter scale error, tolerance band accuracy, over and under estimation tails, agreement with ITU clutter height regimes, and SHAP-based physical plausibility. SHAP 将树冠覆盖、土地覆盖语义和光谱反射率确定为最有影响力的预测因子。 Studies on segmentation derived features, non-forest ablations, and land-cover matched international validation show that open geospatial data can improve clutter modeling at scale without sacrificing interpretability or deployability.