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 3ā6, 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.
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 ā20), November 3ā6, 2020, Seattle, WA, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3397536.3422209