Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation

Nur Hamid; Ari Wibisono; Ahmad Gamal; Ronni Ardhianto; Wisnu Jatmiko

doi:10.1109/TENSYMP50017.2020.9230926

Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation

Nur Hamid, Ari Wibisono, Ahmad Gamal, Ronni Ardhianto, Wisnu Jatmiko

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

2 Citations (Scopus)

Abstract

LiDAR data widely replaces 2-dimensional data for geographic data representation because of its information complexity. One of the LiDAR data processing tasks is semantic segmentation which has been developed by deep learning models. These algorithms use Euclidean distance representation to express the distance between the points, whereas LiDAR data with random properties are not suitable to use this distance representation. Therefore, this study proposes the non-Euclidean distance representation which is adaptively updated using their covariance values. The proposed method results the accuracy of 75.55%, better than the baseline PointNet of 65.08% and Dynamic Graph CNN of 72.56% with the dataset from the author. This performance improvement is because of multiplication with the inverse covariance value of point cloud data increasing the points similarity to the class.

Original language	English
Title of host publication	2020 IEEE Region 10 Symposium, TENSYMP 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1038-1041
Number of pages	4
ISBN (Electronic)	9781728173665
DOIs	https://doi.org/10.1109/TENSYMP50017.2020.9230926
Publication status	Published - 5 Jun 2020
Event	2020 IEEE Region 10 Symposium, TENSYMP 2020 - Virtual, Dhaka, Bangladesh Duration: 5 Jun 2020 → 7 Jun 2020

Publication series

Name	2020 IEEE Region 10 Symposium, TENSYMP 2020

Conference

Conference	2020 IEEE Region 10 Symposium, TENSYMP 2020
Country/Territory	Bangladesh
City	Virtual, Dhaka
Period	5/06/20 → 7/06/20

Keywords

deep learning
graph convolutional network
land cover semantic segmentation
LiDAR data
non-Euclidean

Access to Document

10.1109/TENSYMP50017.2020.9230926

Cite this

Hamid, N., Wibisono, A., Gamal, A., Ardhianto, R., & Jatmiko, W. (2020). Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation. In 2020 IEEE Region 10 Symposium, TENSYMP 2020 (pp. 1038-1041). Article 9230926 (2020 IEEE Region 10 Symposium, TENSYMP 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENSYMP50017.2020.9230926

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title = "Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation",

abstract = "LiDAR data widely replaces 2-dimensional data for geographic data representation because of its information complexity. One of the LiDAR data processing tasks is semantic segmentation which has been developed by deep learning models. These algorithms use Euclidean distance representation to express the distance between the points, whereas LiDAR data with random properties are not suitable to use this distance representation. Therefore, this study proposes the non-Euclidean distance representation which is adaptively updated using their covariance values. The proposed method results the accuracy of 75.55%, better than the baseline PointNet of 65.08% and Dynamic Graph CNN of 72.56% with the dataset from the author. This performance improvement is because of multiplication with the inverse covariance value of point cloud data increasing the points similarity to the class. ",

keywords = "deep learning, graph convolutional network, land cover semantic segmentation, LiDAR data, non-Euclidean",

author = "Nur Hamid and Ari Wibisono and Ahmad Gamal and Ronni Ardhianto and Wisnu Jatmiko",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE Region 10 Symposium, TENSYMP 2020 ; Conference date: 05-06-2020 Through 07-06-2020",

year = "2020",

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doi = "10.1109/TENSYMP50017.2020.9230926",

language = "English",

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Hamid, N, Wibisono, A , Gamal, A, Ardhianto, R & Jatmiko, W 2020, Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation. in 2020 IEEE Region 10 Symposium, TENSYMP 2020., 9230926, 2020 IEEE Region 10 Symposium, TENSYMP 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1038-1041, 2020 IEEE Region 10 Symposium, TENSYMP 2020, Virtual, Dhaka, Bangladesh, 5/06/20. https://doi.org/10.1109/TENSYMP50017.2020.9230926

Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation. / Hamid, Nur; Wibisono, Ari ; Gamal, Ahmad et al.
2020 IEEE Region 10 Symposium, TENSYMP 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1038-1041 9230926 (2020 IEEE Region 10 Symposium, TENSYMP 2020).

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

TY - GEN

T1 - Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation

AU - Hamid, Nur

AU - Wibisono, Ari

AU - Gamal, Ahmad

AU - Ardhianto, Ronni

AU - Jatmiko, Wisnu

PY - 2020/6/5

Y1 - 2020/6/5

N2 - LiDAR data widely replaces 2-dimensional data for geographic data representation because of its information complexity. One of the LiDAR data processing tasks is semantic segmentation which has been developed by deep learning models. These algorithms use Euclidean distance representation to express the distance between the points, whereas LiDAR data with random properties are not suitable to use this distance representation. Therefore, this study proposes the non-Euclidean distance representation which is adaptively updated using their covariance values. The proposed method results the accuracy of 75.55%, better than the baseline PointNet of 65.08% and Dynamic Graph CNN of 72.56% with the dataset from the author. This performance improvement is because of multiplication with the inverse covariance value of point cloud data increasing the points similarity to the class.

AB - LiDAR data widely replaces 2-dimensional data for geographic data representation because of its information complexity. One of the LiDAR data processing tasks is semantic segmentation which has been developed by deep learning models. These algorithms use Euclidean distance representation to express the distance between the points, whereas LiDAR data with random properties are not suitable to use this distance representation. Therefore, this study proposes the non-Euclidean distance representation which is adaptively updated using their covariance values. The proposed method results the accuracy of 75.55%, better than the baseline PointNet of 65.08% and Dynamic Graph CNN of 72.56% with the dataset from the author. This performance improvement is because of multiplication with the inverse covariance value of point cloud data increasing the points similarity to the class.

KW - deep learning

KW - graph convolutional network

KW - land cover semantic segmentation

KW - LiDAR data

KW - non-Euclidean

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Adaptive Update in Deep Learning Algorithms for LiDAR Data Semantic Segmentation

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