An Attention Model for Remote Sensing Landcover ClassificationSimmons, Shelton remote sensingland-cover classificationmulti-spectralLandsatdeep learningvision transformerSpatial Data ScienceMSHerein, the design of a transformer used to perform remote sensing landcover image classification is analyzed. The transformer was designed to use the traditional remote sensing indices to perform the classification. A custom dataset was created for this study using Landsat 8 and 9 OLI imagery. The dataset consists of 50 images of each landcover class; urban, water, vegetation, agricultural, forest, and bare earth with an additional 10 images per class for validation. Each image was classified using the remote sensing indices to produce square clips that are roughly 90 pixels per side. The vision transformer is trained using this custom training set by giving a bonus value to pixels which fall into the target range for the various remote sensing indices and a penalty for those that are classified while outside of the target range. This transformer had an overall accuracy gain of 1% compared to the transformer that didn’t use thresholds. Furthermore, the reward and penalty logic was shown to reduce the training loss of the transformer. Bing Zhou, Thesis Advisor/Co-AdvisorFritz Connor Kessler, Committee MemberAnthony Robinson, Program Head/Chairopen_access2026-07-14T13:02:17Zhttps://etda.libraries.psu.edu/catalog/30497sos6023