Addressing Annotation Imprecision for Tree Crown Delineation Using the
RandCrowns Index
- URL: http://arxiv.org/abs/2105.02186v1
- Date: Wed, 5 May 2021 16:57:23 GMT
- Title: Addressing Annotation Imprecision for Tree Crown Delineation Using the
RandCrowns Index
- Authors: Dylan Stewart, Alina Zare, Sergio Marconi, Ben Weinstein, Ethan White,
Sarah Graves, Stephanie Bohlman, Aditya Singh
- Abstract summary: Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management.
Current evaluation methods do not account for uncertainty in annotations.
We address these limitations using an adaptation of the Rand index for weakly-labeled crown delineation that we call RandCrowns.
- Score: 2.6144305160661228
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Supervised methods for object delineation in remote sensing require labeled
ground-truth data. Gathering sufficient high quality ground-truth data is
difficult, especially when the targets are of irregular shape or difficult to
distinguish from the background or neighboring objects. Tree crown delineation
provides key information from remote sensing images for forestry, ecology, and
management. However, tree crowns in remote sensing imagery are often difficult
to label and annotate due to irregular shape, overlapping canopies, shadowing,
and indistinct edges. There are also multiple approaches to annotation in this
field (e.g., rectangular boxes vs. convex polygons) that further contribute to
annotation imprecision. However, current evaluation methods do not account for
this uncertainty in annotations, and quantitative metrics for evaluation can
vary across multiple annotators. We address these limitations using an
adaptation of the Rand index for weakly-labeled crown delineation that we call
RandCrowns. The RandCrowns metric reformulates the Rand index by adjusting the
areas over which each term of the index is computed to account for uncertain
and imprecise object delineation labels. Quantitative comparisons to the
commonly used intersection over union (Jaccard similarity) method shows a
decrease in the variance generated by differences among multiple annotators.
Combined with qualitative examples, our results suggest that this RandCrowns
metric is more robust for scoring target delineations in the presence of
uncertainty and imprecision in annotations that are inherent to tree crown
delineation. Although the focus of this paper is on evaluation of tree crown
delineations, annotation imprecision is a challenge that is common across
remote sensing of the environment (and many computer vision problems in
general).
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