Noise2Map repurposes diffusion model denoising into a direct predictor for semantic segmentation and change detection tasks in remote sensing, achieving top average ranks on benchmark datasets.
A review of remote sensing image segmentation by deep learning methods
2 Pith papers cite this work. Polarity classification is still indexing.
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Vision Transformers reduce computational operations by an order of magnitude for spatio-temporal vegetation pixel classification while maintaining competitive accuracy and constant parameter count independent of time series length.
citing papers explorer
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Noise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detection
Noise2Map repurposes diffusion model denoising into a direct predictor for semantic segmentation and change detection tasks in remote sensing, achieving top average ranks on benchmark datasets.
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Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers
Vision Transformers reduce computational operations by an order of magnitude for spatio-temporal vegetation pixel classification while maintaining competitive accuracy and constant parameter count independent of time series length.