Author

Sarah Farhadpour

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Comparing feature maps generated using UNet-like CNN, 2 Transformer, Mamba, and hybrid architectures for general

This study compares feature maps produced by semantic segmentation architectures using varying combinations of convolutional neural network (CNN), Transformer, and Mamba selective 26 state space (selective SSM) components with a goal of exploring the following question: does correlation or similarity between the genera...

Aaron E. Maxwell, Sarah Farhadpour, Christopher A. Ramezan · 0 citations

SegFormer and SegFormer-UNet for anthropogenic geomorphic feature extraction from land surface parameters

Three architectures for pixel-level extraction of features from light detection and ranging (lidar)-derived land surface parameters (LSPs) are compared: SegUNet and the UNet typically achieve the best precision and recall balance and the smoothest optimization, and UNet-style architectures with CNN-based decoders gener...

Sarah Farhadpour, Aaron E. Maxwell, Behnam Solouki · 0 citations