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Country-wide high-resolution vegetation height mapping with Sentinel-2
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“While retrieval of vegetation parameters with deep learning has been demonstrated regionally and up to country scale16–19, we scale it up and process the entire global landmass.”
From A high-resolution canopy height model of the Earth (2023) · cites this paper as [16]
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“Our model is based on the fully convolutional neural network architecture proposed in prior work16.”
From A high-resolution canopy height model of the Earth (2023) · cites this paper as [16]
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“These methods have been shown to mitigate the saturation of tall canopies by exploiting texture while not depending on temporal features16.”
From A high-resolution canopy height model of the Earth (2023) · cites this paper as [16]
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“Previous work has demonstrated that without the ability to learn spatial features, the performance drops substantially especially for the tall canopies16.”
From A high-resolution canopy height model of the Earth (2023) · cites this paper as [16]
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“, 2014) and assessment of positions in board games like go or chess (Silver et al., 2018).”
BackgroundDeep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
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Cited by
Sorted by citations
A high-resolution canopy height model of the Earth
Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02206-6 · Cited by 810
Self-citationDigital taxonomist: Identifying plant species in community scientists’ photographs
de Lutio, R., She, Y., D’Aronco, S., Russo, S., Brun, P., Wegner, J. D., & Schindler, K. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.10.002 · Cited by 23
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Metadata
- Authors
- Lang, N., Schindler, K., & Wegner, J. D.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 233
- Pages
- 111347
- ISSN
- 0034-4257
- Cited by
- 202
- References
- 6
- Language
- en
- Funders
- Barry Callebaut Sourcing AG
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