Jackson Cothren
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Professor
Faculty Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Jackson Cothren's research focuses on the application of advanced computational techniques, particularly deep learning and artificial intelligence, to diverse fields. He has investigated methods for aerial image segmentation using multi-resolution transformers, as seen in his work on AerialFormer. His research also extends to semantic scene understanding through fairness domain adaptation, as demonstrated by FREDOM. Cothren has explored direct aerial visual geolocalization with deep neural networks and the use of UAV and ground-based geophysical imagery for evaluating soil heterogeneity's influence on soybean development.
His work includes developing and applying machine learning models for tasks such as video scene graph generation and anticipation (HyperGLM) and simultaneous referring remote sensing segmentation and detection (RSSep). Cothren has also contributed to the broader research landscape by addressing challenges in geospatial data analysis, particularly in the context of COVID-19, and by exploring community-building and infrastructure design for transdisciplinary research, as noted in his publication on dataARC.
Cothren holds an h-index of 13 and has authored 71 publications with 797 citations. He has been a principal investigator or co-principal investigator on six federal grants totaling over $7.7 million, including a significant NSF grant for the Arkansas Smart Transportation Research Incubator. His collaborations include researchers from the University of Arkansas at Fayetteville, such as Pha Nguyen, Thanh-Dat Truong, Trong-Thuan Nguyen, and Chase Rainwater.
Metrics
- h-index: 13
- Publications: 71
- Citations: 809
Selected Publications
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Using Declassified Imagery: Issues and Approaches (2026)
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Data from: Detecting altimetric changes in Arctic landscapes using historical aerial imagery-derived digital elevation models (hDEMs): Case study of the Black Mountain Alluvial Fan Complex, Canada (2025)
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Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking (2025)
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FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding (2025)
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HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation (2025)
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RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection (2025)
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S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling (2025)
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AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation (2024)
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Improving InSAR Accuracy for Slow Deformation and Change Detection with Lidar and GPS (2024)
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FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding (2023)
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Absolute Accuracy Assessment of Maxar's Worldwide 3D Textured Mesh (2022)
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Direct Aerial Visual Geolocalization Using Deep Neural Networks (2021)
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Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19 (2021)
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Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery (2021)
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Current and New Frontiers: Exploring How Place Matters Through Arkansas NIBRS Reporting Practices (2020)
Federal Grants 6 $7,782,453 total
CC* CIRA: Shared Arkansas Research Plan for Community Cyber Infrastructure (SHARP CCI)
E-RISE Rll: Arkansas Smart Transportation Research Incubator through Data Engineering and Science
Collaboration Network
Top Collaborators
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
- S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling
- RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- Direct Aerial Visual Geolocalization Using Deep Neural Networks
- RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
- HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation
- FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding
- Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
- Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19
- Absolute Accuracy Assessment of Maxar's Worldwide 3D Textured Mesh
- Data from: Detecting altimetric changes in Arctic landscapes using historical aerial imagery-derived digital elevation models (hDEMs): Case study of the Black Mountain Alluvial Fan Complex, Canada
- FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
- FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding
- FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
- FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection
- Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
- Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
- Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
- Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
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