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

^*Bu çalışma için yazışmadan sorumlu yazar

Geomatik Mühendisliği Bölümü

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology - Proceedings of the 1st MedGU, Istanbul 2021 Volume 3
Editörler	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
Yayınlayan	Springer Nature
Sayfalar	283-286
Sayfa sayısı	4
ISBN (Basılı)	9783031432170
DOI'lar	https://doi.org/10.1007/978-3-031-43218-7_66
Yayın durumu	Yayınlandı - 2024
Etkinlik	1st International conference on Mediterranean Geosciences Union, MedGU 2021 - Istanbul, Turkey Süre: 25 Kas 2021 → 28 Kas 2021

Yayın serisi

Adı	Advances in Science, Technology and Innovation
ISSN (Basılı)	2522-8714
ISSN (Elektronik)	2522-8722

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	1st International conference on Mediterranean Geosciences Union, MedGU 2021
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	25/11/21 → 28/11/21

Bibliyografik not

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

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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. editör / 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).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

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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.

PY - 2024

Y1 - 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 - Bezzeghoud, Mourad

A2 - Ustuner, Mustafa

A2 - Eshagh, Mehdi

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A2 - Biswas, Arkoprovo

A2 - Gasperini, Luca

A2 - Hinzen, Klaus-Günter

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A2 - Comina, Cesare

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A2 - Polonia, Alina

A2 - Chaminé, Helder I.

PB - Springer Nature

T2 - 1st International conference on Mediterranean Geosciences Union, MedGU 2021

Y2 - 25 November 2021 through 28 November 2021

ER -

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, editörler, 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

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