Channel Attention and Normal-Based Local Feature Aggregation Network (CNLNet): A Deep Learning Method for Predisaster Large-Scale Outdoor Lidar Semantic Segmentation-Reference-Cited by-同舟云学术

Channel Attention and Normal-Based Local Feature Aggregation Network (CNLNet): A Deep Learning Method for Predisaster Large-Scale Outdoor Lidar Semantic Segmentation

Published:2024 Issue: Volume:62 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Chang¹^ORCID,Ge Linlin¹,Xiang Wei²^ORCID,Du Zheyuan¹^ORCID,Zhang Qi³

Affiliation:

1. School of Civil and Environmental Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia

2. School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, VIC, Australia

3. School of Engineering and Design, Technical University of Munich, Munich, Germany

Funder

SmartSat Cooperative Research Centre

Australian Government’s CRC Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10343136.pdf?arnumber=10343136

Reference19 articles.

1. A novel attention-based deep learning method for post-disaster building damage classification

2. A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds

3. PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data

4. Deep Learning for 3D Point Clouds: A Survey

5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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