Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning

Muhammed Enes Atik; Zaide Duran

doi:10.1007/978-3-031-43218-7_66

Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning

Muhammed Enes Atik^*, Zaide Duran

^*Corresponding author for this work

Department of Geomatics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Deep learning methods have been successfully used in image processing and computer vision. Point cloud semantic segmentation is also a current study subject where deep learning is widely used. In this study, Semantic3D, a terrestrial laser scanning data, and Dublin City, an airborne laser scanning data, were used. Random sampling and an effective local feature aggregator (RANDLA-Net) were used as the segmentation algorithm. Precision, recall, F1 score, and overall accuracy were used as evaluation metrics. The overall accuracy is obtained as 0.882 in the Semantic3D dataset and 0.896 in the Dublin City dataset.

Original language	English
Title of host publication	Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3
Editors	Attila Çiner, Zeynal Abiddin Ergüler, Mourad Bezzeghoud, Mustafa Ustuner, Mehdi Eshagh, Hesham El-Askary, Arkoprovo Biswas, Luca Gasperini, Klaus-Günter Hinzen, Murat Karakus, Cesare Comina, Ali Karrech, Alina Polonia, Helder I. Chaminé
Publisher	Springer Nature
Pages	283-286
Number of pages	4
ISBN (Print)	9783031432170
DOIs	https://doi.org/10.1007/978-3-031-43218-7_66
Publication status	Published - 2024
Event	1st International conference on Mediterranean Geosciences Union, MedGU 2021 - Istanbul, Turkey Duration: 25 Nov 2021 → 28 Nov 2021

Publication series

Name	Advances in Science, Technology and Innovation
ISSN (Print)	2522-8714
ISSN (Electronic)	2522-8722

Conference

Conference	1st International conference on Mediterranean Geosciences Union, MedGU 2021
Country/Territory	Turkey
City	Istanbul
Period	25/11/21 → 28/11/21

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Airborne LiDAR
Deep learning
Point cloud
Semantic segmentation
Terrestrial LiDAR

Access to Document

10.1007/978-3-031-43218-7_66

Cite this

Atik, M. E., & Duran, Z. (2024). Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning. In A. Çiner, Z. A. Ergüler, M. Bezzeghoud, M. Ustuner, M. Eshagh, H. El-Askary, A. Biswas, L. Gasperini, K.-G. Hinzen, M. Karakus, C. Comina, A. Karrech, A. Polonia, & H. I. Chaminé (Eds.), Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3 (pp. 283-286). (Advances in Science, Technology and Innovation). Springer Nature. https://doi.org/10.1007/978-3-031-43218-7_66

Atik, Muhammed Enes ; Duran, Zaide. / Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning. Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3. editor / Attila Çiner ; Zeynal Abiddin Ergüler ; Mourad Bezzeghoud ; Mustafa Ustuner ; Mehdi Eshagh ; Hesham El-Askary ; Arkoprovo Biswas ; Luca Gasperini ; Klaus-Günter Hinzen ; Murat Karakus ; Cesare Comina ; Ali Karrech ; Alina Polonia ; Helder I. Chaminé. Springer Nature, 2024. pp. 283-286 (Advances in Science, Technology and Innovation).

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title = "Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning",

abstract = "Deep learning methods have been successfully used in image processing and computer vision. Point cloud semantic segmentation is also a current study subject where deep learning is widely used. In this study, Semantic3D, a terrestrial laser scanning data, and Dublin City, an airborne laser scanning data, were used. Random sampling and an effective local feature aggregator (RANDLA-Net) were used as the segmentation algorithm. Precision, recall, F1 score, and overall accuracy were used as evaluation metrics. The overall accuracy is obtained as 0.882 in the Semantic3D dataset and 0.896 in the Dublin City dataset.",

keywords = "Airborne LiDAR, Deep learning, Point cloud, Semantic segmentation, Terrestrial LiDAR",

author = "Atik, {Muhammed Enes} and Zaide Duran",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 1st International conference on Mediterranean Geosciences Union, MedGU 2021 ; Conference date: 25-11-2021 Through 28-11-2021",

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doi = "10.1007/978-3-031-43218-7_66",

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Atik, ME & Duran, Z 2024, Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning. in A Çiner, ZA Ergüler, M Bezzeghoud, M Ustuner, M Eshagh, H El-Askary, A Biswas, L Gasperini, K-G Hinzen, M Karakus, C Comina, A Karrech, A Polonia & HI Chaminé (eds), Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3. Advances in Science, Technology and Innovation, Springer Nature, pp. 283-286, 1st International conference on Mediterranean Geosciences Union, MedGU 2021, Istanbul, Turkey, 25/11/21. https://doi.org/10.1007/978-3-031-43218-7_66

Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning. / Atik, Muhammed Enes ; Duran, Zaide.
Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3. ed. / Attila Çiner; Zeynal Abiddin Ergüler; Mourad Bezzeghoud; Mustafa Ustuner; Mehdi Eshagh; Hesham El-Askary; Arkoprovo Biswas; Luca Gasperini; Klaus-Günter Hinzen; Murat Karakus; Cesare Comina; Ali Karrech; Alina Polonia; Helder I. Chaminé. Springer Nature, 2024. p. 283-286 (Advances in Science, Technology and Innovation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning

AU - Atik, Muhammed Enes

AU - Duran, Zaide

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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AB - Deep learning methods have been successfully used in image processing and computer vision. Point cloud semantic segmentation is also a current study subject where deep learning is widely used. In this study, Semantic3D, a terrestrial laser scanning data, and Dublin City, an airborne laser scanning data, were used. Random sampling and an effective local feature aggregator (RANDLA-Net) were used as the segmentation algorithm. Precision, recall, F1 score, and overall accuracy were used as evaluation metrics. The overall accuracy is obtained as 0.882 in the Semantic3D dataset and 0.896 in the Dublin City dataset.

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A2 - Ustuner, Mustafa

A2 - Eshagh, Mehdi

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Atik ME , Duran Z. Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning. In Çiner A, Ergüler ZA, Bezzeghoud M, Ustuner M, Eshagh M, El-Askary H, Biswas A, Gasperini L, Hinzen KG, Karakus M, Comina C, Karrech A, Polonia A, Chaminé HI, editors, Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3. Springer Nature. 2024. p. 283-286. (Advances in Science, Technology and Innovation). doi: 10.1007/978-3-031-43218-7_66

Comparison of Semantic Segmentation of Point Clouds Obtained from Different Sensors Using Deep Learning

Abstract

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Bibliographical note

Keywords

Access to Document

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