Update README.md

README.md

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 ---
 license: cc-by-4.0
+language:
+- en
+task_categories:
+- image-to-image
+- image-segmentation
+pretty_name: GeoSR-Bench
+size_categories:
+- 10K<n<100K
 ---
+
+# GeoSR-Bench
+
+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
+
+```text
+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
+
+```text
+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:
+
+```text
+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:
+
+```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:
+
+```python
+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:
+
+```python
+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`:
+
+```python
+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:
+
+```bibtex
+@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}
+}
+```