Congratulations to our labmates: Sarah Walker, Joshua Peeples, Jeff Dale, James Keller and Alina Zare! Their paper, “Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery” was recently accepted to the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). In their… Read More
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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
EXPLAINABLE SYSTEMATIC ANALYSIS FOR SYNTHETIC APERTURE SONAR IMAGERY
Abstract: In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar… 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
CONGRATULATIONS TO CONNOR MCCURLEY FOR BECOMING A PHD CANDIDATE!
Congratulations to our labmate, Connor McCurley, for passing his oral qualifying exam and becoming a PhD candidate! For the remainder of his Ph.D. work, Connor plans to investigate “Discriminative Manifold Embedding with Imprecise, Uncertain and Ambiguous Data.” Great work, Connor!
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
MULTI-TARGET MI-ACE ACCEPTED TO TGRS!
Congratulations to our labmates: Susan K. Meerdink, James Bocinsky, Alina Zare, Nick Kroeger, Connor H. McCurley, Daniel Shats and Paul D. Gader! Their paper, “Multi-Target Multiple Instance Learning for Hyperspectral Target Detection” was recently accepted to IEEE Transactions on Geoscience… Read More
WELCOME NEW UNDERGRADUATE RESEARCH ASSISTANT DANIEL BERTAK!
The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Daniel Bertak! Daniel is majoring in Computer Engineering at the University of Florida and will be investigating machine learning methods for plant root analysis. Welcome to… Read More
WELCOME NEW UNDERGRADUATE RESEARCH ASSISTANT HAYDEN MENGE!
The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Hayden Menge! Hayden is majoring in Computer Engineering at the University of Florida and will be investigating machine learning methods for agricultural applications. Welcome to our… Read More
WELCOME NEW MASTER’S STUDENT AARON ZYGALA!
The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Aaron Zygala! Aaron is pursuing a Master’s degree in Computer Science at the University of Florida and will be working on a new project in collaboration… Read More