Dale Hamilton
Position
Chair, Department of Mathematics/Computer Science; Associate Professor
Dale Hamilton has been an Associate Professor of Computer Science at Northwest Nazarene University since 2013. In addition to teaching a variety of Computer Science courses, Dale is the Primary Investigator on NNU’s NASA funded FireMAP project which is using Machine Learning and small unmanned aircraft system (sUAS) to map wildland fire extent and severity, research which was culminated in his PhD in Computer Science at the University of Idaho, which was awarded in 2018. More recently, Dale has led the expansion of the FireMAP research program to include a collaborative relationship with the USDA Forest Service which has enabled NNU and the Forest Service to map archaeological features across the Boise National Forest.
Prior to coming to Northwest Nazarene University, Dale has spent 13 years as a Managing Software Engineer/Project Manager at Systems for Environmental Management, writing software modeling fire behavior and effects, ecological departure and remote sensing under contract with the USDA Forest Service and Department of Interior.
Publications:
Hamilton, D., Brothers, K*., Jones, S*., Colwell, J., & Winters, J*. (2021). Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning. Remote Sensing, 13(2), 290.
Hamilton, D., Levandovsky, E*., & Hamilton, N*. (2020). Mapping Burn Extent of Large Wildland Fires from Satellite Imagery Using Machine Learning Trained from Localized Hyperspatial Imagery. Remote Sensing, 12(24), 4097
Hamilton, D; Pacheco*, R; Myers, B; Peltzer, B*, (2020) “kNN vs. SVM: a Comparison of Algorithms”, Proceedings of the Fire Continuum-Preparing for the future of wildland fire; 2018 May 21-24; Missoula, MT. Proceedings RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 358 p.
Hamilton D., Hamilton N.*, Myers B. (2019) Evaluation of Image Spatial Resolution for Machine Learning Mapping of Wildland Fire Effects. Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer
Hamilton, D; Myers, B; Branham, J*, (2017) “Evaluation of Texture as an Input of Spatial Context for Machine Learning Mapping of Wildland Fire Effects”. Signal and Image Processing: An International Journal, 8(5).
Hamilton,D; Bowerman, M*; Colwell, J; Donahoe, G; Myers B, (2017) “A Spectroscopic Analysis for Mapping Wildland Fire Effects from Remotely Sensed Imagery”, Journal of Unmanned Vehicle Systems, 5(4), 146-158.
* Indicates student author
Prior to coming to Northwest Nazarene University, Dale has spent 13 years as a Managing Software Engineer/Project Manager at Systems for Environmental Management, writing software modeling fire behavior and effects, ecological departure and remote sensing under contract with the USDA Forest Service and Department of Interior.
Publications:
Hamilton, D., Brothers, K*., Jones, S*., Colwell, J., & Winters, J*. (2021). Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning. Remote Sensing, 13(2), 290.
Hamilton, D., Levandovsky, E*., & Hamilton, N*. (2020). Mapping Burn Extent of Large Wildland Fires from Satellite Imagery Using Machine Learning Trained from Localized Hyperspatial Imagery. Remote Sensing, 12(24), 4097
Hamilton, D; Pacheco*, R; Myers, B; Peltzer, B*, (2020) “kNN vs. SVM: a Comparison of Algorithms”, Proceedings of the Fire Continuum-Preparing for the future of wildland fire; 2018 May 21-24; Missoula, MT. Proceedings RMRS-P-78. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 358 p.
Hamilton D., Hamilton N.*, Myers B. (2019) Evaluation of Image Spatial Resolution for Machine Learning Mapping of Wildland Fire Effects. Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer
Hamilton, D; Myers, B; Branham, J*, (2017) “Evaluation of Texture as an Input of Spatial Context for Machine Learning Mapping of Wildland Fire Effects”. Signal and Image Processing: An International Journal, 8(5).
Hamilton,D; Bowerman, M*; Colwell, J; Donahoe, G; Myers B, (2017) “A Spectroscopic Analysis for Mapping Wildland Fire Effects from Remotely Sensed Imagery”, Journal of Unmanned Vehicle Systems, 5(4), 146-158.
* Indicates student author
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