Jackson Cothren Data-verified

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Federal Grant PI

Professor

Last publication 2025 Last refreshed 2026-05-22

faculty

13 h-index 71 pubs 809 cited

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

  • 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)
  • Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking (2025)
  • FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding (2025)
  • HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation (2025)
    3 citations DOI OpenAlex
  • RSSep: Sequence-to-Sequence Model for Simultaneous Referring Remote Sensing Segmentation and Detection (2025)
    1 citation DOI OpenAlex
  • S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling (2025)
    3 citations DOI OpenAlex
  • AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation (2024)
    61 citations DOI OpenAlex
  • Improving InSAR Accuracy for Slow Deformation and Change Detection with Lidar and GPS (2024)
    1 citation DOI OpenAlex
  • FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding (2023)
    26 citations DOI OpenAlex
  • Absolute Accuracy Assessment of Maxar's Worldwide 3D Textured Mesh (2022)
  • Direct Aerial Visual Geolocalization Using Deep Neural Networks (2021)
    9 citations DOI OpenAlex
  • Challenges and Limitations of Geospatial Data and Analyses in the Context of COVID-19 (2021)
    4 citations DOI OpenAlex
  • Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery (2021)
    42 citations DOI OpenAlex

View all publications on OpenAlex →

Federal Grants 6 $7,782,453 total

Collaboration Network

87 Collaborators 24 Institutions 4 Countries

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