A new explainable DTM generation algorithm with airborne LIDAR data: grounds are smoothly connected eventually

TL;DR

This paper introduces an explainable and reliable airborne LiDAR DTM generation algorithm that considers broader context beyond local neighborhoods, improving accuracy and interpretability in complex environments.

Contribution

The study proposes a novel open-source DTM algorithm that accounts for wider spatial context and offers enhanced explainability and robustness over existing methods.

Findings

Robustness tested in complex environments

Outperforms state-of-the-art algorithms

Results are easily explainable and predictable

Abstract

The digital terrain model (DTM) is fundamental geospatial data for various studies in urban, environmental, and Earth science. The reliability of the results obtained from such studies can be considerably affected by the errors and uncertainties of the underlying DTM. Numerous algorithms have been developed to mitigate the errors and uncertainties of DTM. However, most algorithms involve tricky parameter selection and complicated procedures that make the algorithm's decision rule obscure, so it is often difficult to explain and predict the errors and uncertainties of the resulting DTM. Also, previous algorithms often consider the local neighborhood of each point for distinguishing non-ground objects, which limits both search radius and contextual understanding and can be susceptible to errors particularly if point density varies. This study presents an open-source DTM generation algorithm for airborne LiDAR data that can consider beyond the local neighborhood and whose results are easily explainable, predictable, and reliable. The key assumption of the algorithm is that grounds are smoothly connected while non-grounds are surrounded by areas having sharp elevation changes. The robustness and uniqueness of the proposed algorithm were evaluated in geographically complex environments through tiling evaluation compared to other state-of-the-art algorithms.