Author

S. Graves

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 4

Signature Assays, Organisms & Methods

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Assay:imaging· 4 Approach:deep learning· 3 Research Focus:benchmarking· 2 Research Focus:ecology & biodiversity· 2 Approach:benchmark study· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Organism:plant (other)· 1

Frequent Co-Authors

B. Weinstein· 4 shared S. Bohlman· 4 shared S. Marconi· 4 shared A. Singh· 3 shared A. Zare· 3 shared E. White· 3 shared D. Johnson· 1 shared D. Stewart· 1 shared

Activity by Year

2020-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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4 shared papersEcology

B. Weinstein

Mutual co-authors in cluster

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4 papers

Recent·Most cited

Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model

B. Weinstein, S. Marconi, S. Graves, A. Zare, A. Singh, S. Bohlman, L. Magee, D. Johnson, P. Townsend, E. White

The paper develops a multi-temporal hierarchical model to improve rare tree species classification in airborne imagery, achieving 75% accuracy for 14 species and generating species maps for over 670,000 trees.

bioRxiv · Dec 7, 2022 · PreprintEcology

A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

B. Weinstein, S. Graves, S. Marconi, A. Singh, A. Zare, D. Stewart, S. Bohlman, E. P. White

The paper introduces a benchmark dataset for evaluating individual tree crown delineation algorithms using co-registered remote sensing data and provides an R package for standardized evaluation.

bioRxiv · Nov 16, 2020Ecology

NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns

B. Weinstein, S. Marconi, S. Bohlman, A. Zare, A. Singh, S. Graves, E. White

The paper introduces a dataset of 100 million individual tree crowns derived from remote sensing and deep learning, enabling large-scale forest analysis.

bioRxiv · Sep 8, 2020 · PreprintEcology

Estimating individual level plant traits at scale

S. Marconi, S. Graves, B. Weinstein, S. Bohlman, E. White

This paper develops a method to estimate individual tree traits at scale using remote sensing data, achieving moderate accuracy for several traits and demonstrating improved performance over pixel-level predictions.

bioRxiv · Aug 4, 2020Ecology