Constraint-Based DEM Enhancement Using LiDAR Data at Sparse Highway-Bridge Locations
Abstract
Digital elevation models (DEMs) are essential for infrastructure design and flood modeling, yet publicly available DEMs typically exhibit vertical accuracies of one to three meters, insufficient for these applications. This study develops and systematically evaluates a constraint-based DEM enhancement approach that integrates sparse, high-accuracy light detection and ranging (LiDAR) reference data collected at bridge locations to improve regional DEM quality. The methodology employs nearest-neighbor spatial interpolation to generate elevation correction surfaces from constraint points (points of known accurate elevations), followed by Gaussian smoothing to preserve topographic continuity. The approach was evaluated using LiDAR data from 15 bridge locations in the Austin metropolitan area in Central Texas. Four spatial interpolation methods (nearest neighbor, inverse distance weighting, natural neighbor, and linear interpolation) were compared, with nearest neighbor achieving optimal performance with a 28.79% improvement in mean Root Mean Square Error (RMSE) within the study area. Gaussian filtering with an optimized smoothing parameter ( σ = 0.3 m ) further enhanced accuracy, achieving a mean RMSE to 0.105 m. Spatial configuration analysis across six-, nine-, and 15-bridge configurations revealed critical dependencies: distributed constraint arrangements consistently achieved optimal accuracy with six to eight constraint locations, while clustered configurations (constraint points clustered inside the area of interest) with peripheral constraints exhibited performance degradation despite increasing constraint count. This performance resulted from nearest-neighbor interpolation’s reliance on geometric proximity without terrain similarity consideration. The findings provide practical guidance for transportation agencies and flood management programs seeking to leverage existing infrastructure-derived LiDAR surveys for regional DEM enhancement, demonstrating that strategic constraint placement throughout the area of interest is essential for maximizing performance.