Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
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2025
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| author | Taher, Josef Hyyppä, Eric Hyyppä, Matti Salolahti, Klaara Yu, Xiaowei Matikainen, Leena Kukko, Antero Lehtomäki, Matti Kaartinen, Harri Thurachen, Sopitta Litkey, Paula Luoma, Ville Holopainen, Markus Kong, Gefei Fan, Hongchao Rönnholm, Petri Vaaja, Matti Polvivaara, Antti Junttila, Samuli Vastaranta, Mikko Puliti, Stefano Astrup, Rasmus Kostensalo, Joel Myllymäki, Mari Kulicki, Maksymilian Stereńczak, Krzysztof Pires, Raul de Paula Valbuena, Ruben Carbonell-Rivera, Juan Pedro Torralba, Jesús Chen, Yi-Chen Winiwarter, Lukas Hollaus, Markus Mandlburger, Gottfried Takhtkeshha, Narges Remondino, Fabio Lisiewicz, Maciej Kraszewski, Bartłomiej Liang, Xinlian Chen, Jianchang Ahokas, Eero Karila, Kirsi Vezeteu, Eugeniu Manninen, Petri Näsi, Roope Hyyti, Heikki Pyykkönen, Siiri Hu, Peilun Hyyppä, Juha |
| author_facet | Taher, Josef Hyyppä, Eric Hyyppä, Matti Salolahti, Klaara Yu, Xiaowei Matikainen, Leena Kukko, Antero Lehtomäki, Matti Kaartinen, Harri Thurachen, Sopitta Litkey, Paula Luoma, Ville Holopainen, Markus Kong, Gefei Fan, Hongchao Rönnholm, Petri Vaaja, Matti Polvivaara, Antti Junttila, Samuli Vastaranta, Mikko Puliti, Stefano Astrup, Rasmus Kostensalo, Joel Myllymäki, Mari Kulicki, Maksymilian Stereńczak, Krzysztof Pires, Raul de Paula Valbuena, Ruben Carbonell-Rivera, Juan Pedro Torralba, Jesús Chen, Yi-Chen Winiwarter, Lukas Hollaus, Markus Mandlburger, Gottfried Takhtkeshha, Narges Remondino, Fabio Lisiewicz, Maciej Kraszewski, Bartłomiej Liang, Xinlian Chen, Jianchang Ahokas, Eero Karila, Kirsi Vezeteu, Eugeniu Manninen, Petri Näsi, Roope Hyyti, Heikki Pyykkönen, Siiri Hu, Peilun Hyyppä, Juha |
| contents | Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing, but challenges remain in leveraging deep learning techniques and identifying rare tree species in class-imbalanced datasets. This study addresses these gaps by conducting a comprehensive benchmark of deep learning and traditional shallow machine learning methods for tree species classification. For the study, we collected high-density multispectral ALS data ($>1000$ $\mathrm{pts}/\mathrm{m}^2$) at three wavelengths using the FGI-developed HeliALS system, complemented by existing Optech Titan data (35 $\mathrm{pts}/\mathrm{m}^2$), to evaluate the species classification accuracy of various algorithms in a peri-urban study area located in southern Finland. We established a field reference dataset of 6326 segments across nine species using a newly developed browser-based crowdsourcing tool, which facilitated efficient data annotation. The ALS data, including a training dataset of 1065 segments, was shared with the scientific community to foster collaborative research and diverse algorithmic contributions. Based on 5261 test segments, our findings demonstrate that point-based deep learning methods, particularly a point transformer model, outperformed traditional machine learning and image-based deep learning approaches on high-density multispectral point clouds. For the high-density ALS dataset, a point transformer model provided the best performance reaching an overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065 segments and 92.0% (85.1%) with a larger training set of 5000 segments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms Taher, Josef Hyyppä, Eric Hyyppä, Matti Salolahti, Klaara Yu, Xiaowei Matikainen, Leena Kukko, Antero Lehtomäki, Matti Kaartinen, Harri Thurachen, Sopitta Litkey, Paula Luoma, Ville Holopainen, Markus Kong, Gefei Fan, Hongchao Rönnholm, Petri Vaaja, Matti Polvivaara, Antti Junttila, Samuli Vastaranta, Mikko Puliti, Stefano Astrup, Rasmus Kostensalo, Joel Myllymäki, Mari Kulicki, Maksymilian Stereńczak, Krzysztof Pires, Raul de Paula Valbuena, Ruben Carbonell-Rivera, Juan Pedro Torralba, Jesús Chen, Yi-Chen Winiwarter, Lukas Hollaus, Markus Mandlburger, Gottfried Takhtkeshha, Narges Remondino, Fabio Lisiewicz, Maciej Kraszewski, Bartłomiej Liang, Xinlian Chen, Jianchang Ahokas, Eero Karila, Kirsi Vezeteu, Eugeniu Manninen, Petri Näsi, Roope Hyyti, Heikki Pyykkönen, Siiri Hu, Peilun Hyyppä, Juha Computer Vision and Pattern Recognition Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing, but challenges remain in leveraging deep learning techniques and identifying rare tree species in class-imbalanced datasets. This study addresses these gaps by conducting a comprehensive benchmark of deep learning and traditional shallow machine learning methods for tree species classification. For the study, we collected high-density multispectral ALS data ($>1000$ $\mathrm{pts}/\mathrm{m}^2$) at three wavelengths using the FGI-developed HeliALS system, complemented by existing Optech Titan data (35 $\mathrm{pts}/\mathrm{m}^2$), to evaluate the species classification accuracy of various algorithms in a peri-urban study area located in southern Finland. We established a field reference dataset of 6326 segments across nine species using a newly developed browser-based crowdsourcing tool, which facilitated efficient data annotation. The ALS data, including a training dataset of 1065 segments, was shared with the scientific community to foster collaborative research and diverse algorithmic contributions. Based on 5261 test segments, our findings demonstrate that point-based deep learning methods, particularly a point transformer model, outperformed traditional machine learning and image-based deep learning approaches on high-density multispectral point clouds. For the high-density ALS dataset, a point transformer model provided the best performance reaching an overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065 segments and 92.0% (85.1%) with a larger training set of 5000 segments. |
| title | Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.14337 |