Jason A. Tullis
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor / Department Chair
Also affiliated: University of South Carolina (2003–2005)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Jason A. Tullis is a professor at the University of Arkansas at Fayetteville. His research focuses on the application of machine learning and artificial intelligence to geoscientific data, particularly in agricultural and environmental contexts. He has investigated methods for object-based change detection using correlation image analysis and image segmentation, as well as the synergistic use of Lidar and color aerial photography for mapping urban imperviousness. Tullis has also explored the impact of Lidar post-spacing on digital elevation model accuracy and flood zone delineation, and employed remote sensing and GIS-assisted approaches for landscape epidemiology, such as in the study of West Nile virus.
His work includes the development of cyberinfrastructure for managing geoscientific AI workflows, exemplified by the "Geoweaver" project. Tullis also contributes to discussions on ethical replicability and reproducibility in GIScience. His scholarship metrics include an h-index of 13, with 41 total publications and 1,240 total citations. He has collaborated with researchers including Jackson Cothren, Malcolm Williamson, Lawton Lanier Nalley, and Harrison Smith, all from the University of Arkansas at Fayetteville.
Metrics
- h-index: 13
- Publications: 41
- Citations: 1,242
Positions
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Professor / Department Chair 2017–presentUniversity of Arkansas Department of Geosciences ORCID
Selected Publications
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Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield (2026)
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Spatiotemporal Characterization of Soybean Phenology in the Arkansas Delta Region Using Multi-Source Remotely Sensed Data from 2002 to 2020 (2025)
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Framework for Mapping Sublimation Features on Mars’ South Polar Cap Using Object-Based Image Analysis (2025)
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A review of cyberinfrastructure for machine learning and big data in the geosciences (2022)
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Context for Reproducibility and Replicability in Geospatial Unmanned Aircraft Systems (2022)
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Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19 (2021)
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Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience (2020)AGU Fall Meeting Abstracts OpenAlex
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Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows (2020)
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Geographic information system (GIS)-based image analysis for assessing growth of Physarum polycephalum on a solid medium (2015)
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Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing (2013)
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A geological characterization of Ligeia Mare in the northern polar region of Titan (2013)
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A remote sensing and GIS-assisted landscape epidemiology approach to West Nile virus (2013)
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Spatial Scale Management Experiments Using Optical Aerial Imagery and LIDAR Data Synergy (2010)
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Per-segment Aboveground Forest Biomass Estimation Using LIDAR-Derived Height Percentile Statistics (2009)
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Modeling red oak borer, Enaphalodes rufulus (Haldeman), damage using in situ and ancillary landscape data (2007)
Collaboration Network
Top Collaborators
- Object‐based change detection using correlation image analysis and image segmentation
- Empirical versus Model‐based Atmospheric Correction of Digital Airborne Imaging Spectrometer Hyperspectral Data
- Spatial Scale Management Experiments Using Optical Aerial Imagery and LIDAR Data Synergy
- Development of a remote sensing change detection system based on neighborhood correlation image analysis and intelligent knowledge-based systems
- A remote sensing and GIS-assisted landscape epidemiology approach to West Nile virus
- Yearly Extraction of Central America's Land Cover for Carbon Flux Monitoring
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows
- A review of cyberinfrastructure for machine learning and big data in the geosciences
- Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience
- Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows
- A review of cyberinfrastructure for machine learning and big data in the geosciences
- Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience
- Object‐based change detection using correlation image analysis and image segmentation
- Development of a remote sensing change detection system based on neighborhood correlation image analysis and intelligent knowledge-based systems
- Per-segment Aboveground Forest Biomass Estimation Using LIDAR-Derived Height Percentile Statistics
- Modeling red oak borer, Enaphalodes rufulus (Haldeman), damage using in situ and ancillary landscape data
- A remote sensing and GIS-assisted landscape epidemiology approach to West Nile virus
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Spatial Scale Management Experiments Using Optical Aerial Imagery and LIDAR Data Synergy
- Rapid Assessment of Storm-Surge Inundation after Hurricane Katrina Utilizing a Modified Distance Interpolation Approach
- A geological characterization of Ligeia Mare in the northern polar region of Titan
- Framework for Mapping Sublimation Features on Mars’ South Polar Cap Using Object-Based Image Analysis
- Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows
- Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience
- Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows
- Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience
- Per-segment Aboveground Forest Biomass Estimation Using LIDAR-Derived Height Percentile Statistics
- Empirical versus Model‐based Atmospheric Correction of Digital Airborne Imaging Spectrometer Hyperspectral Data
- Empirical versus Model‐based Atmospheric Correction of Digital Airborne Imaging Spectrometer Hyperspectral Data
- Spatial Scale Management Experiments Using Optical Aerial Imagery and LIDAR Data Synergy
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