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Mapping oil palm density at country scale: An active learning approach

Rodríguez, Andrés C., D'Aronco, Stefano, Schindler, Konrad, Wegner, Jan D.·2021·Remote Sensing of Environment
DOI 10.1016/j.rse.2021.112479
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  • The approach scales up previous regional deep learning methods to process the entire global landmass.

    “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 [19]

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Research works printed but not linked: 40

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  14. Ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International conference on machine learning, PMLR, 448
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  17. Kellenberger, 2019, Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning, IEEE Trans. Geosci. Remote Sens., 57, 9524, 10.1109/TGRS.2019.2927393 · DOI
  18. Laurin, 2013, Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa, Int. J. Appl. Earth Obs. Geoinf., 21, 7
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Metadata

Authors
Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2021-08
Volume
261
Pages
112479
ISSN
0034-4257
DOI
10.1016/j.rse.2021.112479
Cited by
36
References
5
Language
en
Licence

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