GeoSR-Bench is the first SR benchmark that directly measures how super-resolved remote sensing imagery improves performance on land cover segmentation, infrastructure mapping, and biophysical variable estimation rather than relying on fidelity metrics.
Recursive generalization transformer for image super-resolution
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SAT introduces density and isolation-based token aggregation to enable efficient global attention in super-resolution transformers, claiming up to 0.22 dB PSNR gain and 27% FLOP reduction over PFT.
citing papers explorer
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Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration
GeoSR-Bench is the first SR benchmark that directly measures how super-resolved remote sensing imagery improves performance on land cover segmentation, infrastructure mapping, and biophysical variable estimation rather than relying on fidelity metrics.
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SAT: Selective Aggregation Transformer for Image Super-Resolution
SAT introduces density and isolation-based token aggregation to enable efficient global attention in super-resolution transformers, claiming up to 0.22 dB PSNR gain and 27% FLOP reduction over PFT.