About

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona. He holds a Ph.D. from Simon Fraser University, obtained in 1999. His research interests include computer vision, machine learning, scientific applications, and multimedia data. Barnard's work focuses on advancing understanding and techniques within these areas, contributing to the development of intelligent systems and data analysis methods. He is actively involved in the academic community, supporting undergraduate and graduate programs, and engaging in research that intersects artificial intelligence and multimedia data analysis.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Information Retrieval
• Mathematics
• Engineering

Selected publications

• Assessing Inundation Semantic Segmentation Models Trained on High- versus Low-Resolution Labels using FloodPlanet, a Manually Labeled Multi-Sourced High-Resolution Flood Dataset

  Journal of Remote Sensing · 2025-01-01 · 3 citations

  articleOpen access

  Flooding impacts more people than any other environmental hazard, causing extensive economic and social impact. Leveraging satellite data and deep learning substantially improves flood monitoring and, potentially, management. However, deep learning efforts are frequently constrained by the limited availability of high-quality training and validation datasets, as well as the resolution, spatial coverage, and temporal limitations of inundation observation from public sensors. To address these challenges and contribute to the future development of geo-foundation models requiring extensive data and validation, we curate and publicly release FloodPlanet, a manually labeled inundation dataset [ 1 ]. FloodPlanet stands out for its manual annotations based on 3-m high-resolution commercial data from PlanetScope, and diverse ecoregions and flood event coverage. The dataset includes 366 hand-annotated labels, each 1,024 × 1,024 pixels, with corresponding Sentinel-1 and Sentinel-2 imagery, covering 19 global flood events from 2017 to 2020. Employing a “leave-one-region-out” cross-validation approach with a baseline UNet model, we achieved a mean intersection over union score (IoU) of 0.691 (SD: 0.227) for inundation detection across all events with PlanetScope, which is around 20% higher compared to Sentinel-1 and Sentinel-2 from the same event. Comparative analysis using PlanetScope labels to train models with Sentinel-1 and Sentinel-2 data revealed that FloodPlanet labels improve public sensor-based inundation detection by up to 15.6% (SD: 0.242) in IoU. These results imply that even if commercial data are too costly for near real-time inference applications, using some commercial data to train public sensor models could be an important lower-cost investment to increase accuracy.

  PublisherDOI

• Hippocampal blood oxygenation predicts choices about everyday consumer experiences: A deep-learning approach

  Proceedings of the National Academy of Sciences · 2025-07-07

  articleOpen access

  This research investigates the neurophysiological mechanisms of experiential versus monetary choices under risk. While ventral striatum and insula activity are instrumental in predicting monetary choices, we find that hippocampal activity plays a key role in predicting experiential choices, which we theorize is due to its role in retrieving autobiographical memories. This neurophysiological differentiation clarifies observed variations in risk preferences between experiential and monetary prospects and highlights the importance of domain-specific neurophysiological processes in shaping human decision-making.

  PublisherDOI

• Quantifying Leaf Symptoms of Sorghum Charcoal Rot in Images of Field-Grown Plants Using Deep Neural Networks

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-04-19

  preprintOpen access

  ABSTRACT Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red-green-blue (RGB) images of sorghum plants exhibiting symptoms of infection. EfficientNet-B3 and a fully convolutional network (FCN) emerged as the top-performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet-B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their processing time decreased exponentially. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a base for drone-based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web-based application where users can easily analyze their own images. Core ideas Automated phenotyping tools are required for the efficient detection and quantification of charcoal rot of sorghum. Classification and segmentation models can distinguish between concurrent plant stresses with similar symptoms. Larger image patch sizes generally improve model performance and reduce processing time.

  PublisherOA PDFDOI

• PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics

  Computers and Electronics in Agriculture · 2024-04-17 · 25 citations

  articleOpen access
  PublisherOA PDFDOI

• Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks

  The Plant Phenome Journal · 2024-06-27 · 4 citations

  articleOpen access

  Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

  PublisherOA PDFDOI

• The ToMCAT Dataset

  2023-01-01

  article
  PublisherDOI

• Probabilistic Modeling of Human Teams to Infer False Beliefs

  arXiv (Cornell University) · 2023-10-19 · 1 citations

  preprintOpen accessSenior author

  We develop a probabilistic graphical model (PGM) for artificially intelligent (AI) agents to infer human beliefs during a simulated urban search and rescue (USAR) scenario executed in a Minecraft environment with a team of three players. The PGM approach makes observable states and actions explicit, as well as beliefs and intentions grounded by evidence about what players see and do over time. This approach also supports inferring the effect of interventions, which are vital if AI agents are to assist human teams. The experiment incorporates manipulations of players' knowledge, and the virtual Minecraft-based testbed provides access to several streams of information, including the objects in the players' field of view. The participants are equipped with a set of marker blocks that can be placed near room entrances to signal the presence or absence of victims in the rooms to their teammates. In each team, one of the members is given a different legend for the markers than the other two, which may mislead them about the state of the rooms; that is, they will hold a false belief. We extend previous works in this field by introducing ToMCAT, an AI agent that can reason about individual and shared mental states. We find that the players' behaviors are affected by what they see in their in-game field of view, their beliefs about the meaning of the markers, and their beliefs about which meaning the team decided to adopt. In addition, we show that ToMCAT's beliefs are consistent with the players' actions and that it can infer false beliefs with accuracy significantly better than chance and comparable to inferences made by human observers.

  PublisherOA PDFDOI

• Classifying Astronomical Transients Using Only Host Galaxy Photometry

  The Astrophysical Journal · 2023-01-01 · 13 citations

  articleOpen access

  Abstract The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will discover tens of thousands of extragalactic transients each night. The high volume of alerts demands immediate classification of transient types in order to prioritize observational follow-ups before events fade away. We use host galaxy features to classify transients, thereby providing classification upon discovery. In contrast to past work that focused on distinguishing Type Ia and core-collapse supernovae (SNe) using host galaxy features that are not always accessible (e.g., morphology), we determine the relative likelihood across 12 transient classes based on only 19 host apparent magnitudes and colors from 10 optical and IR photometric bands. We develop both binary and multiclass classifiers, using kernel density estimation to estimate the underlying distribution of host galaxy properties for each transient class. Even in this pilot study, and ignoring relative differences in transient class frequencies, we distinguish eight transient classes at purities significantly above the 8.3% baseline (based on a classifier that assigns labels uniformly and at random): tidal disruption events (TDEs; 48% ± 27%, where ± indicates the 95% confidence limit), SNe Ia-91bg (32% ± 18%), SNe Ia-91T (23% ± 11%), SNe Ib (23% ± 13%), SNe II (17% ± 2%), SNe IIn (17% ± 6%), SNe II P (16% ± 4%), and SNe Ia (10% ± 1%). We demonstrate that our model is applicable to LSST and estimate that our approach can accurately classify 59% of LSST alerts expected each year for SNe Ia, Ia-91bg, II, Ibc, SLSN-I, and TDEs. Our code and data set are publicly available.

  PublisherOA PDFDOI

• Modular Procedural Generation for Voxel Maps

  Lecture notes in computer science · 2022-01-01

  book-chapterOpen accessSenior author
  PublisherOA PDFDOI

• In memoriam of Emily Butler, 1963–2023

  Cognition & Emotion · 2022-11-17

  articleSenior author
  PublisherDOI

Frequent coauthors

• Brian Funt

  Simon Fraser University

  27 shared

• Alon Efrat

  Alexandru Ioan Cuza University

  13 shared

• Quanfu Fan

  Amazon (Germany)

  11 shared

• David Forsyth

  11 shared

• Ariyan Zarei

  University of Arizona

  9 shared

• Vlad C. Cardei

  8 shared

• Yimian Dai

  8 shared

• Joseph Schlecht

  7 shared