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Individual tree-crown detection in RGB imagery using self-supervised deep learning neural networks

Ben. G. Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan White
doi: https://doi.org/10.1101/532952
Ben. G. Weinstein
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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Sergio Marconi
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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Stephanie Bohlman
2School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA
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Alina Zare
3Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
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Ethan White
1Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA
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Abstract

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in true color, or red/green blue (RGB) imagery using a deep learning detection network. Individual crown delineation is a persistent challenge in studies of forested ecosystems and has primarily been addressed using three-dimensional LIDAR. We show that deep learning models can leverage existing lidar-based unsupervised delineation approaches to initially train an RGB crown detection model, which is then refined using a small number of hand-annotated RGB images. We validate our proposed approach using an open-canopy site in the National Ecological Observation Network (NEON). Our results show that combining LIDAR and RGB methods in a self-supervised model improves predictions of trees in natural landscapes. The addition of a small number of hand-annotated trees improved performance over the initial self-supervised model. While undercounting of individual trees in complex canopies remains an area of development, deep learning can increase the performance of remotely sensed tree surveys.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 28, 2019.
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Individual tree-crown detection in RGB imagery using self-supervised deep learning neural networks
Ben. G. Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan White
bioRxiv 532952; doi: https://doi.org/10.1101/532952
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Individual tree-crown detection in RGB imagery using self-supervised deep learning neural networks
Ben. G. Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan White
bioRxiv 532952; doi: https://doi.org/10.1101/532952

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