Category: News

Join us on March 24th for a FREE ONLINE webinar hosted by Headwall and the University of Florida!  This webinar is an all-day event which will focus on “Recent and Ongoing Hyperspectral Remote Sensing Forestry and Plant Research”.  Also, our very own Dr. Susan Meerdink will be presenting on Multi-Target Multiple Instance Learning for Hyperspectral […]

Abstract: In remote sensing, it is often challenging to acquire or collect a large dataset that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study site’s spatial area and accessibility, errors in the global positioning system (GPS), and mixed pixels caused by an image’s spatial resolution. […]

Our labmate and MLSL post-doctoral researcher, Dr. Susan Meerdink, recently presented at the Phenome 2020 in Tucson, AZ! The purpose of Phenome is to bring together a diverse community of researchers to enable collaboration, emphasizing data collection and organization, as well as data processing and the computational and statistical frameworks needed to understand complex biological […]

Dr. Alina Zare recently presented as a keynote speaker at the Phenome 2020 conference in Tucson, AZ! The Phenome conference is a gathering of a multidisciplinary community comprising plant biologists, ecologists, engineers, agronomists, computational scientists, and representatives from U.S. federal agencies which is focused on the methodologies and technologies that enable the study of plant […]

Congratulations to Gatorsense alumnas Hudanyun Sheng for accepting a research position at the Donald Danforth Plant Science Center in St. Louis, MO! We can’t wait to see all of the exciting things you do, Hudanyun!

The Machine Learning and Sensing Lab is excited to welcome our newest lab member, Jason Chen! Jason is a third year Computer Science major at the University of Florida who will be working on new hardware and software for monitoring insect bioacoustics. Additionally, Jason will be investigating new opportunities for public outreach. Welcome to our […]

Machine Learning and Sensing Lab collaborators Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare and Ethan White recently had a paper accepted to Ecological Informatics!   The paper discusses the utilization of data from multiple sites to train generalized tree-crown detectors.  Check it out here!

Machine Learning and Sensing Lab Alumni, Anand Seethepalli, and collaborators recently had a paper accepted to Plant Phenomics.  The article discusses an innovative platform to help collect consistent images of root crowns for phenotyping.  Check it out here! 

Understanding and managing forests is crucial to understanding and potentially mitigating the effects of climate change, invasive species, and shifting land use on natural systems and human society. However, collecting data on individual trees in the field is expensive and time consuming, which limits the scales at which this crucial data is collected. Remotely sensed […]

Abstract: The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process […]