ForestSemantic: a dataset for semantic learning of forest from close-range sensing-Reference-Cited by-同舟云学术

ForestSemantic: a dataset for semantic learning of forest from close-range sensing

Published:2024-03-21 Issue: Volume: Page:1-27
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Liang Xinlian¹^ORCID,Qi Hanwen¹^ORCID,Deng Xuejie¹,Chen Jianchang¹^ORCID,Cai Shangshu¹^ORCID,Zhang Qingjun¹,Wang Yunsheng²^ORCID,Kukko Antero²^ORCID,Hyyppä Juha²^ORCID

Affiliation:

1. The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

2. Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Espoo, Finland

Funder

the Natural Science Fund

Research and Development

This work is supported by the Natural Science Fund of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2024.2313325

Reference34 articles.

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2. SEMANTIC LABELING OF ALS POINT CLOUDS FOR TREE SPECIES MAPPING USING THE DEEP NEURAL NETWORK POINTNET++

3. Laser scanning reveals potential underestimation of biomass carbon in temperate forest

4. A Two-Stage Approach for Individual Tree Segmentation From TLS Point Clouds

5. Charles, R. Q., H. Su, M. Kaichun, and L. J. Guibas. 2017. “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 77–85. https://doi.org/10.1109/CVPR.2017.16.

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