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PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

January 19, 2022

Abstract: Understanding a plant’s root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying […]

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PRMI accepted to AI for Agriculture and Food Systems (AIAFS), 2022!

January 19, 2022

Congratulations to our labmates and collaborators: Weihuang Xu, Guohao Yu, Yiming Cui, Romain Gloaguen, Alina Zare, Jason Bonnette, Joel Reyes-Cabrera, Ashish Rajurkar, Diane Rowland, Roser Matamala, Julie D. Jastrow, Thomas E. Juenger, Felix B. Fritschi! Their paper, “PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study”, was recently accepted to AI for Agriculture […]

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Data Science Competition For Cross-Site Delineation And Classification Of Individual Trees From Airborne Remote Sensing Data

August 27, 2021

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 […]

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Benchmark Dataset Accepted To Plos Computational Biology!

June 10, 2021

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.  […]

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A BENCHMARK DATASET FOR INDIVIDUAL TREE CROWN DELINEATION IN CO-REGISTERED AIRBORNE RGB, LIDAR AND HYPERSPECTRAL IMAGERY FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK

November 18, 2020

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 […]

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 »

NEON TREE CROWNS DATASET

July 17, 2020

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, […]

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