I am Ph.D. candidate at the University of Missouri–Columbia. I build deep learning-based computer vision models for remote sensing applications with an emphasis on semanic segmentation.

• Differential Morphological Profile Neural Networks for Semantic Segmentation
  David Huangal, J. Alex Hurt - arXiv, 2025. [Link]
  Extending DMPNets to modern segmentation backbones; Hybrid RGB+DMP models able surpass non‑DMP baselines on iSAID.

• Evaluation of Road Segmentation Techniques on Visible and Infrared Low‑Altitude UAS Imagery
  David Huangal, Grant J. Scott, Stanton R. Price - IGARSS 2022. [Link]
  Benchmark of road segmentation on visible/IR UAS imagery; presented in Session TU2.MMA (session chair).

• Evaluating Deep Road Segmentation Techniques for Low‑Altitude UAS Imagery
  David Huangal, Jeffrey Dale, J. Alex Hurt, Trevor M. Bajkowski, James M. Keller, Grant J. Scott, Stanton R. Price - SPIE DCS 2020. [Link]
  U‑Net family evaluation for road/no‑road segmentation in low‑altitude UAS scenes.

• Semantic Segmentation of Burned Areas in Sentinel‑2 Satellite Imagery Using Transformer and Convolutional Attention Networks
  Anes Ouadou, David Huangal, Mariam Alshehri, Grant J. Scott, J. Alex Hurt - IEEE JSTARS, 2025. [Link]

• Semantic Segmentation of Burned Areas in Sentinel‑2 Satellite Images Using Deep Learning Models
  Anes Ouadou, David Huangal, J. Alex Hurt, Grant J. Scott - IGARSS 2023. [Link]

• Maneuverability Hazard Detection and Localization in Low-Altitude UAS Imagery
  J. Alex Hurt, David Huangal, Jeffery Dale, Trevor M. Bajkowski, James Keller, Grant J. Scott, Stanton R. Price - SPIE DCS 2021. [Link]

• Enabling Machine‑Assisted Visual Analytics for High‑Resolution Remote Sensing Imagery With Enhanced Benchmark Meta‑Dataset Training of NAS Neural Networks
  J. Alex Hurt, David Huangal, Curt H. Davis, Grant J. Scott - IEEE Big Data 2020. [Link]

Full list (11+): see Google Scholar.