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… Read More
Tag: remote sensing
BENCHMARK DATASET ACCEPTED TO PLOS COMPUTATIONAL BIOLOGY!
Congratulations to our labmates and collaborators: Ben Weinstein, Sarah Graves, Sergio Marconi, Aditya Singh, Alina Zare, Dylan Stewart, Stephanie Bohlman and Ethan P. White! Their paper, “A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and… Read More
RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation
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… Read More
WEAKLY-LABELED RAND INDEX ACCEPTED TO IGARSS!
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… Read More
THE WEAKLY-LABELED RAND INDEX
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… Read More
A REMOTE SENSING DERIVED DATA SET OF 100 MILLION INDIVIDUAL TREE CROWNS FOR THE NATIONAL ECOLOGICAL OBSERVATORY NETWORK
Abstract: Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote… Read More
A BENCHMARK DATASET FOR INDIVIDUAL TREE CROWN DELINEATION IN CO-REGISTERED AIRBORNE RGB, LIDAR AND HYPERSPECTRAL IMAGERY FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK
Abstract: Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been… Read More
TREE CROWNS DATASET NOW AVAILABLE!
We are happy to announce the publication of a new dataset! The NEON Tree Crowns Dataset is a collection of individual tree crown estimates for 100 million trees from 37 geographic sites across the United States. This dataset provides predicted… Read More
NEON TREE CROWNS DATASET
Abstract: The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction… Read More
ENSEMBLE CORRDET ACCEPTED TO IET SMART GRID!
Congratulations to our labmates and collaborators, Keerthiraj Nagaraj, Sheng Zou, Cody Ruben, Surya Dhulipala, Allen Starke, Arturo Bretas, Alina Zare , and Janise McNair! Their paper, “Ensemble CorrDet with Adaptive Statistics for Bad Data Detection,” was accepted to IET Smart Grid. … Read More