Dataset and code for the paper "Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration"
Python
8
25 commits
updated Jun 2, 2026
Dataset and code for the paper:
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration [arXiv]
GeoSR-Bench is a large-scale benchmark for evaluating remote sensing super-resolution models beyond conventional image fidelity metrics, such as PSNR and SSIM. GeoSR-Bench evaluates whether super-resolved images improve downstream Earth observation tasks.
The complete GeoSR-Bench dataset and trained models are available on Hugging Face: https://huggingface.co/datasets/ai-spatial/GeoSR-Bench
GeoSR-Bench covers two cross-platform super-resolution settings:
It also includes multiple downstream task datasets for evaluating the practical utility of super-resolution outputs, such as:
Dataset and code for the paper "Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration"
Python
8
25 commits
updated Jun 2, 2026
Dataset and code for the paper:
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration [arXiv]
GeoSR-Bench is a large-scale benchmark for evaluating remote sensing super-resolution models beyond conventional image fidelity metrics, such as PSNR and SSIM. GeoSR-Bench evaluates whether super-resolved images improve downstream Earth observation tasks.
The complete GeoSR-Bench dataset and trained models are available on Hugging Face: https://huggingface.co/datasets/ai-spatial/GeoSR-Bench
GeoSR-Bench covers two cross-platform super-resolution settings:
It also includes multiple downstream task datasets for evaluating the practical utility of super-resolution outputs, such as:
