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Armin Hadzik presents work on handling image and label resolution mismatch in remote sensing. Collaboration with Scott Workman and Usman Rafiq. Overhead imagery diversity poses challenges for semantic segmentation algorithms due to resolution differences between input imagery and labels. They aim to generate detailed predictions using low resolution labels and high resolution exemplar data during training. Cost of collecting pixel-level annotations matching image quality is rising as high resolution imagery becomes more common.