A deep neural network based on U-Net for classifying airborne LiDAR data

Fırat Uray; Abdullah Varlık

doi:10.51489/tuzal.1778332

A deep neural network based on U-Net for classifying airborne LiDAR data

Abstract

LiDAR point cloud classification has always been a difficult task. Deep learning (DL) models have recently been widely used in computer vision studies, thanks to rapid advances in machine learning technology. In recent years, deep neural networks (DNN) have been used for classification and segmentation of LiDAR point clouds. We proposed a Fully Convolutional Network (FCN) architecture that can classify LiDAR data. This workflow is built around converting a three-dimensional (3D) LiDAR point cloud to a single two-dimensional (2D) multi-channel image. We tested the performance of our neural network model on the DALES airborne LiDAR dataset. The results showed that our network model performed well when compared to the LAStools software, with an average F1 score of 86% for our best model across all three classes.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Fırat Uray
0000-0001-9555-3190
Türkiye

Abdullah Varlık ^*
0000-0003-2072-3313
Türkiye

Publication Date

March 25, 2026

Submission Date

September 5, 2025

Acceptance Date

November 7, 2025

Published in Issue

Year 2026 Volume: 8

DOI

https://doi.org/10.51489/tuzal.1778332

IZ

https://izlik.org/JA93CA55XR

Cite

RIS / Bibtex

APA

Uray, F., & Varlık, A. (2026). A deep neural network based on U-Net for classifying airborne LiDAR data. Turkish Journal of Remote Sensing, 8, 1-10. https://doi.org/10.51489/tuzal.1778332