Tag: evaluation

Congratulations to our labmates and collaborators: Dylan Stewart, Alina Zare, Sergio Marconi, Ben Weinstein, Ethan White, Sarah Grave, Stephanie Bohlman and Aditya Singh! Their paper, “RANDCROWNS: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation”, was recently accepted to IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. In the paper, […]

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 targets are of irregular shape or difficult to distinguish from background or neighboring objects. Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management. However, tree crowns […]

Congratulations to our labmates: Dylan Stewart, Anna Hampton, Alina Zare, Jeff Dale and James Keller!  Their paper, “The Weakly-Labeled Rand Index” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their paper, the authors introduce an approach to quantify superpixel segmentation performance. Whereas traditional evaluation approaches require crisp segmentations, the […]

Abstract: Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there are many approaches to quantify performance in standard crisp segmentation schemes, drawing hard boundaries in […]