Multi-Size Voxel Cube (MSVC) Algorithm-A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00381825" target="_blank" >RIV/68407700:21110/25:00381825 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/rs17040615" target="_blank" >https://doi.org/10.3390/rs17040615</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs17040615" target="_blank" >10.3390/rs17040615</a>

Result language

angličtina

Original language name

Multi-Size Voxel Cube (MSVC) Algorithm-A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network

Original language description

When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep neural network. This is based on the voxelization of the point cloud, the classification of individual voxels as ground or non-ground using surrounding voxels (a "voxel cube" of 9 x 9 x 9 voxels), and the gradual reduction in voxel size, allowing the acquisition of custom-level detail and highly rugged terrain from dense point clouds. The MSVC performance on two dense point clouds, capturing highly rugged areas with dense vegetation cover, was compared with that of the widely used cloth simulation filter (CSF) using manually classified terrain as the reference. MSVC consistently outperformed the CSF filter in terms of the correctly identified ground points, correctly identified non-ground points, balanced accuracy, and the F-score. Another advantage of this filter lay in its easy adaptability to any type of terrain, enabled by the utilization of machine learning. The only disadvantage lay in the necessity to manually prepare training data. On the other hand, we aim to account for this in the future by producing neural networks trained for individual landscape types, thus eliminating this phase of the work.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20101 - Civil engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Remote Sensing

ISSN

2072-4292

e-ISSN

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Volume of the periodical

17

Issue of the periodical within the volume

4

Country of publishing house

CH - SWITZERLAND

Number of pages

24

Pages from-to

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UT code for WoS article

001430863700001

EID of the result in the Scopus database

2-s2.0-85219212725