GeoSR-Bench

🚧 Dataset Upload in Progress

GeoSR-Bench is currently being uploaded and reorganized.

Some files, metadata, and subsets may still be incomplete or subject to change.

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Dataset Description

GeoSR-Bench directly connects super-resolution (SR) with downstream Earth monitoring tasks, moving beyond conventional fidelity-based evaluation. It comprises spatially co-located, temporally aligned, and quality-controlled image pairs from about 36,000 locations across diverse land covers, spanning spatial resolutions from 500 m to 0.6 m. It is designed to evaluate whether improved image resolution from SR models translates into better downstream performance for tasks such as land cover segmentation, infrastructure mapping, and biophysical variable estimation. GeoSR-Bench includes two cross-platform super-resolution tasks:

• MODIS → Landsat-8
• Sentinel-2 → NAIP

For each task, the dataset is organized into two types of subsets:

1. Super-resolution-only datasets
   These subsets include paired lower-resolution and higher-resolution remote sensing images without downstream task labels. They are designed for training SR models.

2. Downstream task datasets
   These subsets include paired lower-resolution and higher-resolution images together with task-specific labels. They are designed to finetune SR models and evaluate whether super-resolved images improve downstream Earth monitoring tasks, such as land cover segmentation, infrastructure mapping, and biophysical variable estimation.

Each sample may contain:

• A lower-resolution image
• A higher-resolution reference image
• A downstream task label, when available
• Metadata, when available

GeoSR-Bench is intended to support research on task-aware super-resolution, cross-platform learning, and remote sensing foundation models.

Folder Structure

The dataset contains both SR-only datasets and downstream task datasets.

SR-only dataset

SRDatasetName/
├── lr/ or modis/ or s2/
│   ├── lr_0.tif
│   ├── lr_1.tif
│   └── ...
├── hr/ or l8/ or naip/
│   ├── hr_0.tif
│   ├── hr_1.tif
│   └── ...
├── meta/
│   ├── meta_0.json
│   ├── meta_1.json
│   └── ...
└── SRDatasetName_split_all.csv

Downstream task dataset

DownstreamDatasetName/
├── s2/ or modis/
│   ├── s2_0.tif
│   ├── s2_1.tif
│   └── ...
├── naip/ or l8/
│   ├── naip_0.tif
│   ├── naip_1.tif
│   └── ...
├── label/
│   ├── label_0.tif
│   ├── label_1.tif
│   └── ...
├── meta/
│   ├── meta_0.json
│   ├── meta_1.json
│   └── ...
└── DownstreamDatasetName_split_all.csv

Split Files

Each subset includes a CSV file describing the image paths and data split.

SR-only datasets are intended for training super-resolution models and do not have predefined train/validation/test split files.

For downstream task datasets, the CSV contains:

LR image,HR image,Label,split
DownstreamDatasetName/s2/s2_0.tif,DownstreamDatasetName/naip/naip_0.tif,DownstreamDatasetName/label/label_0.tif,training
DownstreamDatasetName/s2/s2_1.tif,DownstreamDatasetName/naip/naip_1.tif,DownstreamDatasetName/label/label_1.tif,validation
DownstreamDatasetName/s2/s2_2.tif,DownstreamDatasetName/naip/naip_2.tif,DownstreamDatasetName/label/label_2.tif,test

Data Fields

Column	Description
LR image	Path to the lower-resolution input image
HR image	Path to the higher-resolution reference image
Label	Path to the downstream task label
split	Dataset split: training, validation, or test

File Formats

• Images are stored as GeoTIFF files (.tif).
• Labels are stored as GeoTIFF files (.tif).
• Metadata files, when available, are stored as JSON files (.json).
• Split files are stored as CSV files (.csv).

GeoTIFF files retain geospatial metadata such as coordinate reference system, transform, resolution, and spatial extent.

Usage

You can read the split CSV using Python:

import pandas as pd

csv_path = "DownstreamDatasetName/DownstreamDatasetName_split_all.csv"
df = pd.read_csv(csv_path)

train_df = df[df["split"] == "training"]
val_df = df[df["split"] == "validation"]
test_df = df[df["split"] == "test"]

print(len(train_df), len(val_df), len(test_df))

For SR-only datasets, use the LR image and HR image columns:

sample = train_df.iloc[0]

lr_path = sample["LR image"]
hr_path = sample["HR image"]

print(lr_path)
print(hr_path)

For downstream task datasets, use the LR image, HR image, and Label columns:

sample = train_df.iloc[0]

lr_path = sample["LR image"]
hr_path = sample["HR image"]
label_path = sample["Label"]

print(lr_path)
print(hr_path)
print(label_path)

You can read GeoTIFF files using rasterio:

import rasterio

with rasterio.open("DownstreamDatasetName/s2/s2_0.tif") as src:
    image = src.read()
    crs = src.crs
    transform = src.transform

print(image.shape)
print(crs)
print(transform)

Intended Use

This dataset is intended for research on:

• Remote sensing image super-resolution
• Downstream task-aware image restoration
• Land cover mapping
• Infrastructure mapping
• Biophysical variable estimation
• Cross-platform Earth observation learning
• Geo-foundation models

Citation

If you use this dataset, please cite:

@article{li2026beyond,
  title={Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration},
  author={Li, Zhili and Chai, Kangyang and Wang, Zhihao and Jia, Xiaowei and Li, Yanhua and Mai, Gengchen and Skakun, Sergii and Manocha, Dinesh and Xie, Yiqun},
  journal={arXiv preprint arXiv:2605.00310},
  year={2026}
}