Articles | Volume 21, issue 11
https://doi.org/10.5194/bg-21-2909-2024
https://doi.org/10.5194/bg-21-2909-2024
Research article
 | 
14 Jun 2024
Research article |  | 14 Jun 2024

From simple labels to semantic image segmentation: leveraging citizen science plant photographs for tree species mapping in drone imagery

Salim Soltani, Olga Ferlian, Nico Eisenhauer, Hannes Feilhauer, and Teja Kattenborn

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Cited articles

Affouard, A., Goëau, H., Bonnet, P., Lombardo, J.-C., and Joly, A.: Pl@ntnet app in the era of deep learning, in: ICLR: International Conference on Learning Representations, April 2017, Toulon, France, ffhal-01629195f, 2017. a, b
Bayraktar, E., Basarkan, M. E., and Celebi, N.: A low-cost UAV framework towards ornamental plant detection and counting in the wild, ISPRS J. Photogramm., 167, 1–11, https://doi.org/10.1016/j.isprsjprs.2020.06.012, 2020. a
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Bouguettaya, A., Zarzour, H., Kechida, A., and Taberkit, A. M.: Deep learning techniques to classify agricultural crops through UAV imagery: A review, Neural Comput. Appl., 34, 9511–9536, 2022. a
Braga, G., J. R., Peripato, V., Dalagnol, R., P. Ferreira, M., Tarabalka, Y., OC Aragão, L. E., F. de Campos Velho, H., Shiguemori, E. H., and Wagner, F. H.: Tree crown delineation algorithm based on a convolutional neural network, Remote Sens., 12, 1288, https://doi.org/10.3390/rs12081288, 2020. a
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Short summary
In this research, we developed a novel method using citizen science data as alternative training data for computer vision models to map plant species in unoccupied aerial vehicle (UAV) images. We use citizen science plant photographs to train models and apply them to UAV images. We tested our approach on UAV images of a test site with 10 different tree species, yielding accurate results. This research shows the potential of citizen science data to advance our ability to monitor plant species.
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