DeepAI AI Chat
Log In Sign Up

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

by   Hunsoo Song, et al.

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.


page 8

page 13

page 16

page 17

page 18

page 20


Towards an unsupervised large-scale 2D and 3D building mapping with LiDAR

A 2D and 3D building map provides invaluable information for understandi...

Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties

Urban-oriented autonomous vehicles require a reliable perception technol...

NCARD: Improving Neighborhood Construction by Apollonius Region Algorithm based on Density

Due to the increased rate of information in the present era, local ident...

Uncertainty-aware LiDAR Panoptic Segmentation

Modern autonomous systems often rely on LiDAR scanners, in particular fo...

Point wise or Feature wise? Benchmark Comparison of Public Available LiDAR Odometry Algorithms in Urban Canyons

Robust and precise localization is essential for the autonomous system w...