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Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring

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Advances in Soft Computing (MICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14392))

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Abstract

This work leverages neural radiance fields and remote sensing for forestry applications. Here, we show neural radiance fields offer a wide range of possibilities to improve upon existing remote sensing methods in forest monitoring. We present experiments that demonstrate their potential to: (1) express fine features of forest 3D structure, (2) fuse available remote sensing modalities and (3), improve upon 3D structure derived forest metrics. Altogether, these properties make neural fields an attractive computational tool with great potential to further advance the scalability and accuracy of forest monitoring programs.

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Acknowledgements

Research presented in this article was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project number GRR0CSRN.

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Authors and Affiliations

  1. Los Alamos National Laboratory, Los Alamos, NM, 48124, USA

    Juan Castorena

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  1. Juan Castorena
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Correspondence to Juan Castorena .

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Editors and Affiliations

  1. Center for Computing Research, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, Mexico

    Hiram Calvo

  2. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Lourdes Martínez-Villaseñor

  3. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Hiram Ponce

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Castorena, J. (2024). Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Soft Computing. MICAI 2023. Lecture Notes in Computer Science(), vol 14392. Springer, Cham. https://doi.org/10.1007/978-3-031-47640-2_23

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  • DOI: https://doi.org/10.1007/978-3-031-47640-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47639-6

  • Online ISBN: 978-3-031-47640-2

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