A. Zare

Individual tree crown maps for the National Ecological Observatory Network

The paper uses computer vision and deep neural networks to predict individual tree species, crown characteristics, and locations across 24 sites in the National Ecological Observatory Network.

HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

HyperPRI provides a dataset of hyperspectral and RGB images of underground plant roots for non-destructive analysis, showing that combining spectral and spatial data improves segmentation.

Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model

The paper develops a multi-temporal hierarchical model to improve rare tree species classification in airborne imagery, achieving 75% accuracy for 14 species and generating species maps for over 670,000 trees.

Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network

The paper develops a continental-scale tree species classifier using NEON data, showing better accuracy than site-specific models and highlighting challenges in species classification across diverse ecosystems.

Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation

This paper introduces JOSHUA, a CNN architecture with spatial histogram layers for adipose tissue segmentation in histological images, showing improved performance over baseline models.

Spatial and Texture Analysis of Root System Distribution with Earth Mover's Distance (STARSEED)

STARSEED quantitatively and visually analyzes root system distribution changes across genotypes and soil moisture levels using Earth Movers Distance.

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

This paper runs a data science competition to improve methods for delineating and classifying individual trees from remote sensing data across multiple sites.

FIBRIN POLYMER ON THE SURFACE OF BIOMATERIAL IMPLANTS DRIVES THE FOREIGN BODY REACTION

This study identifies fibrin polymer as the key biological substrate driving the foreign body reaction by showing reduced macrophage fusion and fibrous capsule formation in fibrinogen-deficient mice.

Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks

This paper develops a semi-supervised deep learning method to detect tree crowns in RGB imagery using unsupervised LIDAR data and a small number of hand-annotated images, achieving accurate predictions in natural landscapes.

Evaluation of Postharvest Senescence in Broccoli Via Hyperspectral Imaging

This study uses hyperspectral imaging and machine learning to estimate glucosinolate levels in broccoli, aiming to detect postharvest senescence and improve freshness assessment.

A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

The paper introduces a benchmark dataset for evaluating individual tree crown delineation algorithms using co-registered remote sensing data and provides an R package for standardized evaluation.

NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns

The paper introduces a dataset of 100 million individual tree crowns derived from remote sensing and deep learning, enabling large-scale forest analysis.

Geographic Generalization in Airborne RGB Deep Learning Tree Detection

This paper evaluates the generalization of RGB deep learning tree detection models across different forest types using data from the National Ecological Observatory Network, finding that a universal model performs as well as site-specific models.

RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping

RhizoVision Crown is an open-source platform for high-throughput root crown phenotyping in crops, validated with simulated images and used on soybean and wheat, showing species-specific heritability.