Tag: NEON

Abstract: Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a […]

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 hyperspectral imagery from the National Ecological Observation Network”, was recently accepted to PLOS Computational Biology.  […]

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 sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in […]

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 proposed, their performance is typically not compared based on standard data or evaluation metrics, making […]

  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 bounding boxes, tree heights, crown areas, class labels and confidence scores for images taken from […]

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 includes height, crown area, and spatial location. Links: Citation: B. Weinstein, S. Marconi, A. Zare, […]

Abstract: Tree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated against data from multiple forest types simultaneously. This makes it difficult to determine the generalization […]