@inproceedings{53bbe17f95b64d8c809608f2539ef2ee,
title = "3D Edge Convolution in Deep Neural Network Implementation for Land Cover Semantic Segmentation of Airborne LiDAR Data",
abstract = "3-dimensional data contains more informative visualization than a 2-dimensional one. LiDAR sensor produces 3D data or point cloud data. There have been many implementations of LiDAR data such as for building detection, urban area modeling, and land cover analysis. This study will analyze land cover because of its substantial benefits. The purpose of this study is to produce semantic segmentation of land cover from LiDAR data by implementing EdgeConv Algorithm from Dynamic Graph Convolutional Neural Network (DGCNN). The dataset in this study is LiDAR data of Kupang, one of the areas in Indonesia. This work achieves the average accuracy of 67.76% for DGCNN better than the state-of-the-art method PointNet (previous method) with 64.97% by implementing the point cloud dataset from LiDAR data of Kupang. This is because the edge convolution could recognize the global shape structure and local neighborhood information so that it could improve the segmentation performance result.",
keywords = "3-dimensional edge detection, 3D CNN, edge convolution, land cover, lidar, semantic segmentation",
author = "Nur Hamid and Ari Wibisono and Ma'sum, {Muhammad Anwar} and Roni Ardhianto and Aniati Murni and Wisnu Jatmiko and Ahmad Gamal",
year = "2019",
month = jul,
doi = "10.1109/ACIRS.2019.8935980",
language = "English",
series = "2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "216--220",
booktitle = "2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019",
address = "United States",
note = "4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019 ; Conference date: 13-07-2019 Through 15-07-2019",
}