Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence

• URL: http://arxiv.org/abs/2607.14127v1
• Date: Fri, 19 Jun 2026 19:08:52 GMT
• Title: Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence
• Abstract summary: Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis.<n>We present an interpretable, globally deployable machine learning framework for predicting RCH from open geospatial data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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.

Related papers

• Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors [0.0]
  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.<n>Local feature importance showed 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.<n>Results provide optioneering guidance and insight over satellite-derived predictive products relevance in distinct contexts, which global machine learning or neural network models cannot offer.
  arXiv  Detail & Related papers  (2026-08-18T14:20:53Z)
• Air Quality Downscaling with Station-Guided Pseudo-Supervision [60.31045749983062]
  Super-resolving coarse atmospheric fields to local PM$_2.5$ variations is uniquely challenged by a mismatch in spatial support.<n>We present a station-guided framework for high-resolution PM$_2.5$ downscaling over Europe.
  arXiv  Detail & Related papers  (2026-07-06T16:32:25Z)
• Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments [47.53030013880309]
  Successful robot automation requires accurate global localization to support repeatability, task planning, goal specification, and safe operation.<n>We present Meridian, a method for matching high-level metric-semantic primitives across aerial images and ground robot RGB-D camera data.<n>We demonstrate that our algorithm can localize a ground robot across a wide variety of environments, including an autonomous driving dataset, a park and campus area, and a wilderness camp.
  arXiv  Detail & Related papers  (2026-06-04T15:52:40Z)
• Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement [46.13526676100776]
  We introduce Paired-CSLiDAR, a cross-source aerial-ground LiDAR benchmark for single-scan pose refinement.<n>The benchmark contains 12,683 ground-aerial pairs across 6 evaluation sites.<n>We propose Residual-Guided Stratified Registration (RGSR), a training-free, geometry-only refinement pipeline.
  arXiv  Detail & Related papers  (2026-05-01T13:14:20Z)
• Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors [1.5484595752241122]
  Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers.<n>This study examines how geographic information from alternative data sources can be incorporated into actuarial models for Motor Third Party Liability (MTPL) claim prediction.
  arXiv  Detail & Related papers  (2026-04-23T17:44:52Z)
• Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection [48.120690574682875]
  We present a metric RGB-based geometric-semantic reconstruction and traversability analysis framework for aerial-to-ground hidden space inspection.<n>Experiments on a tethered UAV-UGV platform demonstrate reliable deployment-zone identification in hidden space scenarios.
  arXiv  Detail & Related papers  (2026-03-15T22:20:59Z)
• CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation [1.1237223647481338]
  We propose a dual encoder architecture that independently extracts modality-specific features from optical and Synthetic Aperture Radar (SAR) imagery.<n>This fusion mechanism highlights complementary spatial and textural features, enabling the network to better capture detailed and diverse land cover patterns.<n>We also introduce a metric-driven reasoning module generated by a Small Language Model (Phi-3), which generates expert-level, sample-specific justifications for model predictions.
  arXiv  Detail & Related papers  (2025-09-15T14:10:52Z)
• TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation [65.74990259650984]
  We introduce TerraFM, a scalable self-supervised learning model that leverages globally distributed Sentinel-1 and Sentinel-2 imagery.<n>Our training strategy integrates local-global contrastive learning and introduces a dual-centering mechanism.<n>TerraFM achieves strong generalization on both classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench.
  arXiv  Detail & Related papers  (2025-06-06T17:59:50Z)
• Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product [41.94295877935867]
  Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts.<n> Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination conditions.<n>NASA's Observational Products for End-Users from Remote Sensing Analysis (OPERA) project released the near-global Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) dataset in October 2023.<n>In this work, we utilize this new dataset to systematically analyze land surface disturbances.
  arXiv  Detail & Related papers  (2025-01-15T20:24:18Z)
• Radio Map Prediction from Aerial Images and Application to Coverage Optimization [46.870065000932016]
  We focus on predicting path loss radio maps using convolutional neural networks.<n>We show that state-of-the-art models developed for existing radio map datasets can be effectively adapted to this task.<n>We introduce a new model dubbed UNetDCN that achieves on par or better performance compared to the state-of-the-art with reduced complexity.
  arXiv  Detail & Related papers  (2024-10-07T09:19:20Z)
• High-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages [0.0]
  Estimating forest aboveground biomass at fine scales has become increasingly important for greenhouse gas estimation. Here we address common obstacles including selection of training data, the investigation of regional or coverage specific bias and error, and map patterns at multiple scales. Our model was overall accurate (% RMSE 13-33%), had very low bias (MBE $leq$ $pm$5 Mg ha$-1$), explained most field-observed variation.
  arXiv  Detail & Related papers  (2022-05-17T17:53:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.