NEON NIST data science evaluation challenge: methods and results of team Conor
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Abstract
The NIST DSE Plant Identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. To compete in the competition, I created a pipeline to perform three remote sensing tasks. First, a NDVI- and height-thresholded watershed segmentation was performed to identify individual tree crowns using LIDAR height measurements. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset's dimensionality through PCA and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.
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2018. NEON NIST data science evaluation challenge: methods and results of team Conor. PeerJ Preprints 6:e26977v1 https://doi.org/10.7287/peerj.preprints.26977v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This is a submission to PeerJ for review.
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Competing Interests
The author declares that they have no competing interests.
Author Contributions
Conor A McMahon conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
The code which I wrote to support my submission for the competition is available at:
Funding
The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program. The ECODSE competition was supported, in part, by a research grant from NIST IAD Data Science Research Program to D.Z. Wang, E.P. White, and S. Bohlman, by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through grant GBMF4563 to E.P. White, and by an NSF Dimension of Biodiversity program grant (DEB-1442280) to S. Bohlman. These funding sources allowed the collection of the data used in the competition and the specification of the competition rules. The authors received no resources from these organizations outside of the data provided for the competition. The data provided for the competition were provided by the National Ecological Observatory Network as described in the Funding Statement. Provision of this data was the only manner in which resources were provided by that organization.
