FAST-GC: A Fully Adaptive Self-Tuning Ground Classification Algorithm for Multi-Platform LiDAR Sensors

Ground-point classification (GPC) is essential for light detection and ranging (LiDAR)-derived terrain and vegetation products, yet most of the well-established GPC algorithms often require parameter calibration across varying terrain, vegetation, point densities, and sensor modalities—airborne laser scanning (ALS), unoccupied aerial vehicle laser scanning (ULS), mobile laser scanning (MLS), and terrestrial laser scanning (TLS). This study introduces FAST-GC, a fully adaptive, self-tuning, parameter-free GPC algorithm designed for ALS, ULS, MLS, and TLS LiDAR sensors. FAST-GC was evaluated against cloth simulation filtering (CSF), progressive TIN densification (PTD), and progressive morphological filtering (PMF) across benchmark sites (n = 6), with additional terrain-complexity, slope, and sampling-density assessments. FAST-GC maintained high ground completeness with limited commission errors, particularly under complex topography. At slopes of 70–80°, the FAST-GC-derived digital terrain model (DTM) mean absolute error (MAE) was 1.00 m, compared with 3.12–12.75 m for the reference counterparts—CSF, PMF, and PTD. Across 20 ALS sites, reducing median point density from 9.84 to 3.84 points/m² maintained F1 ≈ 0.97, intersection over union (IoU) ≈ 0.95, and Matthews correlation coefficient (MCC) ≈ 0.85, while DTM MAE increased slightly from 0.07 to 0.11 m. FAST-GC-derived DTM (0.5 m) from concurrent ALS, ULS, and TLS showed strong cross-platform agreement (R² and concordance correlation coefficient (CCC) ≥ 0.99; RMSE = 0.10–0.16 m). Processing 382 ALS tiles (~23 km²) further demonstrated scalable execution and automated terrain-product generation. These findings establish FAST-GC as a reproducible, sensor-aware framework that preserves terrain morphology across heterogeneous LiDAR datasets without scene-specific parameter calibration.