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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.01932 |
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| _version_ | 1866910364029943808 |
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| author | Li, Lei Zhang, Tianfang Jiang, Zhongyu Yang, Cheng-Yen Hwang, Jenq-Neng Oehmcke, Stefan Gominski, Dimitri Pierre Johannes Gieseke, Fabian Igel, Christian |
| author_facet | Li, Lei Zhang, Tianfang Jiang, Zhongyu Yang, Cheng-Yen Hwang, Jenq-Neng Oehmcke, Stefan Gominski, Dimitri Pierre Johannes Gieseke, Fabian Igel, Christian |
| contents | Accurate and consistent methods for counting trees based on remote sensing data are needed to support sustainable forest management, assess climate change mitigation strategies, and build trust in tree carbon credits. Two-dimensional remote sensing imagery primarily shows overstory canopy, and it does not facilitate easy differentiation of individual trees in areas with a dense canopy and does not allow for easy separation of trees when the canopy is dense. We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees. We compare a deep learning approach to counting trees in forests using 3D airborne LiDAR data and 2D imagery. The approach is compared with state-of-the-art algorithms, like operating on 3D point cloud and 2D imagery. We empirically evaluate the different methods on the NeonTreeCount data set, which we use to define a tree-counting benchmark. The experiments show that FuseCountNet yields more accurate tree counts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tree Counting by Bridging 3D Point Clouds with Imagery Li, Lei Zhang, Tianfang Jiang, Zhongyu Yang, Cheng-Yen Hwang, Jenq-Neng Oehmcke, Stefan Gominski, Dimitri Pierre Johannes Gieseke, Fabian Igel, Christian Computer Vision and Pattern Recognition Accurate and consistent methods for counting trees based on remote sensing data are needed to support sustainable forest management, assess climate change mitigation strategies, and build trust in tree carbon credits. Two-dimensional remote sensing imagery primarily shows overstory canopy, and it does not facilitate easy differentiation of individual trees in areas with a dense canopy and does not allow for easy separation of trees when the canopy is dense. We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees. We compare a deep learning approach to counting trees in forests using 3D airborne LiDAR data and 2D imagery. The approach is compared with state-of-the-art algorithms, like operating on 3D point cloud and 2D imagery. We empirically evaluate the different methods on the NeonTreeCount data set, which we use to define a tree-counting benchmark. The experiments show that FuseCountNet yields more accurate tree counts. |
| title | Tree Counting by Bridging 3D Point Clouds with Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.01932 |