Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction

Published in ACM SIGSPATIAL 2020, 2020

Recommended citation: Takayuki Shinohara, Haoyi Xiu, and Masashi Matsuoka. 2020. Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction. In 28th International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2020), November 3ā€“6, 2020, Seattle, WA, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3397536.3422209 https://dl.acm.org/doi/10.1145/3397536.3422209

This paper is about a novel semantic segmentation method for raw full waveform lidar data, using spatial deep learning method.

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Recommended citation: Takayuki Shinohara, Haoyi Xiu, and Masashi Matsuoka. 2020. Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction. In 28th International Conference on Advances in Geographic Information Systems (SIGSPATIAL ā€™20), November 3ā€“6, 2020, Seattle, WA, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3397536.3422209