发表机构
U.S. Geological Survey（美国地质调查局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用激光雷达衍生地形智能预测卫星地面站选址的代表性杂波高度，提出可解释机器学习框架，用多种数据训练，选LightGBM，模型误差降低超60%，评估相关标准，SHAP识别关键预测因子，证明开放数据可改善杂波建模。
AI 中文摘要
代表性杂波高度（RCH）是无线电传播和干扰分析中的关键参数，当前做法常依赖固定杂波高度，存在问题。本文提出可解释的全球可部署机器学习框架，用美国地质调查局3D高程计划的激光雷达衍生标签等训练模型预测RCH。定义RCH并评估多种回归器，选择LightGBM。最终模型平均绝对误差1.79m，R^2 = 0.765。还评估了与射频规划相关标准，SHAP识别出最具影响力预测因子。研究表明开放地理空间数据可改善杂波建模。
英文摘要
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. We present an interpretable, globally deployable machine learning framework for predicting RCH from open geospatial data. 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. The final model achieves a mean absolute error of 1.79m and an R^2=0.765, reducing absolute error by more than 60% relative to the ITU baseline. 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 identifies tree canopy cover, land-cover semantics, and spectral reflectance as the most influential predictors. 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.
CommentsAccepted at the 2026 IEEE 22nd International Conference on Automation Science and Engineering (IEEE CASE 2026)
