README.md

---
pretty_name: SEN2NEON
license: cc-by-4.0
tags:
  - remote-sensing
  - geospatial
  - sentinel-2
  - super-resolution
  - multispectral
  - earth-observation
task_categories:
  - image-to-image
task_ids:
  - super-resolution
annotations_creators:
  - no-annotation
language_creators:
  - other
language: []
multilinguality:
  - other
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    default: true
    data_files:
      - split: validation
        path: metadata.parquet
---

# SEN2NEON
LR/HR Multispectral Super-Resolution Dataset for benchmarking and research.  
![Example](assets/sen2neon_banner.png)

---

## Dataset Summary
SEN2NEON provides paired **low-resolution (LR, 10 m)** and **high-resolution (HR)** GeoTIFF tiles for validating super-resolution (SR) models in remote sensing. The LR product contains observed Sentinel-2 Level-2A reflectances; the HR references are derived from NEON AVIRIS-NG hyperspectral acquisitions. Each product shares the same spatial footprint and is pixel-aligned. Companion Parquet/JSONL/CSV indexes provide paths for both HR resolutions plus per-tile provenance, acquisition timing, geometry, cloud/nodata statistics, and land-cover labels for **stratified evaluation** (e.g., Forest vs Built-up).

The code, examples, and validation workflows can be found at [ESAOpenSR/SEN2NEON](https://github.com/ESAOpenSR/SEN2NEON).

> **Release correction — 20 August 2026:** A previous dataset upload mistakenly provided a linearized 10 m NEON product as `lr`. This was corrected on 20 August 2026. The canonical LR files are now in `s2_l2a_10m/` and contain the original Sentinel-2 observations exported through Google Earth Engine. The incorrect product is excluded from this release. The quantitative LR/HR consistency values reported in the paper were calculated using the real Sentinel-2 values.

> **HR resolution:** The **2.5 m HR product is the canonical SEN2NEON reference described and evaluated in the paper**. It remains the default in the metadata and companion code. We additionally provide aligned 1 m HR tiles because they are produced by our processing workflow and may be useful to others. The 1 m product is supplementary and does not redefine the published benchmark or its reported results.

- **Modality:** 12-band multispectral GeoTIFFs
- **Tasks:** Super-resolution (image-to-image), benchmarking, and analysis
- **Scale:** 2,269 aligned tile IDs with one LR and two HR products
- **Alignment:** Canonical 2.5 m HR is 4× LR; supplementary 1 m HR is 10× LR; both are integer, isotropic, and pixel-aligned
- **Geo:** Projected UTM zones per tile; WGS84 centroids are included in the metadata
- **Split:** One `validation` split; this is a benchmark, not a globally representative training corpus

---

## Repository Layout
```text
.
├── README.md
├── DATASET_RELEASE_NOTES.md
├── assets/
│   └── sen2neon_banner.png
├── metadata.parquet             # default HF Dataset/Viewer index
├── metadata.jsonl               # equivalent line-delimited JSON index
├── metadata.csv                 # equivalent tabular index
├── s2_l2a_10m.sha256            # SHA-256 manifest for LR tiles
├── s2_l2a_10m/                  # observed Sentinel-2 LR, 12×256×256
├── neon_2.5m_linearized/        # canonical paper HR, 12×1024×1024
└── neon_1m_linearized/          # supplementary workflow HR, 12×2560×2560
```

Relative paths in all metadata indexes match the on-Hub layout. The Parquet index is the default source for the Hugging Face Dataset Viewer and `load_dataset`; JSONL and CSV are retained as portable equivalents.

---

## Record Schema
Each record describes one aligned tile and its LR/canonical/supplementary HR paths. Core fields are:

```json
{
  "id": "<stem>",
  "name": "<filename.tif>",
  "split": "validation",
  "lr": "s2_l2a_10m/<file>.tif",
  "hr": "neon_2.5m_linearized/<file>.tif",
  "hr_2_5m_path": "neon_2.5m_linearized/<file>.tif",
  "hr_1m_path": "neon_1m_linearized/<file>.tif",
  "hr_available_resolutions_m": [2.5, 1.0],
  "hr_canonical_resolution_m": 2.5,
  "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"],
  "lon": 0.0,
  "lat": 0.0,
  "LC_detail_id": 0,
  "LC_detail_text": "<class>",
  "LC_superclass_id": 0,
  "LC_superclass_text": "<class>"
}
```

Additional fields record the exact Sentinel-2 and NEON asset identifiers, acquisition times, temporal separation, cloud score, CRS, reflectance scaling, and the native Sentinel-2 resolution associated with each band. Per-product `hr_2_5m_*` and `hr_1m_*` fields provide raster dimensions, pixel size, nodata value, affine transform, and canonical/supplementary status. The legacy `hr` and generic `hr_*` fields remain aliases for the canonical 2.5 m product for backward compatibility.

Notes:

- `split` is set to `"validation"` for this release.
- `lon`/`lat` are WGS84 centroids. The enriched index also provides the explicit aliases `centroid_lon`/`centroid_lat`.
- Land-cover fields come from the categorical land-cover raster used during preprocessing.
- All raster products contain reflectance stored as `uint16` and scaled by 10,000.

---

## Quick Download
Using the Hub snapshot cache (resumable, selective patterns):

```bash
pip install -U huggingface_hub
python - <<'PY'
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="isp-uv-es/SEN2NEON",
    repo_type="dataset",
    local_dir="./data/sen2neon",
    allow_patterns=[
        "README.md",
        "DATASET_RELEASE_NOTES.md",
        "metadata.*",
        "s2_l2a_10m.sha256",
        "s2_l2a_10m/**",
        "neon_2.5m_linearized/**",
    ],
)
PY
```

This example downloads the canonical 2.5 m product. Replace `neon_2.5m_linearized/**` with `neon_1m_linearized/**` for only the supplementary 1 m product, or include both patterns to download both.

---

## Load with 🤗 Datasets
The default configuration loads the metadata index without downloading all imagery:

```python
from datasets import load_dataset

ds = load_dataset("isp-uv-es/SEN2NEON", split="validation")
row = ds[0]
print(row["id"], row["lr"])
print(row["hr"])             # canonical 2.5 m alias
print(row["hr_2_5m_path"])   # explicit canonical path
print(row["hr_1m_path"])     # supplementary path
```

The LR and HR path columns intentionally remain relative path strings. Hugging Face's standard `Image` decoder relies on PIL, which cannot faithfully decode these 12-band GeoTIFFs. Download individual pairs lazily and read them with `rasterio`:

```python
from huggingface_hub import hf_hub_download
import rasterio

lr_path = hf_hub_download(
    repo_id="isp-uv-es/SEN2NEON",
    filename=row["lr"],
    repo_type="dataset",
)
hr_key = "hr"  # use "hr_1m_path" for the supplementary 1 m product
hr_path = hf_hub_download(
    repo_id="isp-uv-es/SEN2NEON",
    filename=row[hr_key],
    repo_type="dataset",
)

with rasterio.open(lr_path) as src:
    lr = src.read()  # (12, 256, 256)
with rasterio.open(hr_path) as src:
    hr = src.read()  # (12, 1024, 1024) by default; 2560×2560 for 1 m
```

---

## Load with PyTorch (Local Files)
After downloading into `./data/sen2neon/`, use the CSV-driven loader from the companion code repository:

```python
from data.dataset import SEN2NEON
from torch.utils.data import DataLoader

root = "./data/sen2neon"
csv_path = f"{root}/metadata.csv"

ds = SEN2NEON(
    csv_path=csv_path,
    root_dir=root,
    hr_resolution=2.5,  # canonical default; use 1 for supplementary HR
    crop_size_lr=None,
)
loader = DataLoader(ds, batch_size=2, shuffle=True, num_workers=4, pin_memory=True)

batch = next(iter(loader))
lr, hr, meta = batch["lr"], batch["hr"], batch["meta"]
print(lr.shape, hr.shape)
```

---

## Land-cover Integration
Each sample is joined with **land-cover** information derived from an external categorical land-cover raster covering the study area. The LR tile footprint is reprojected to the land-cover CRS; the **mode** value within that window is taken as the label.

- `LC_detail_id` / `LC_detail_text`: fine-grained class (for example, 41 → “Deciduous”).
- `LC_superclass_id` / `LC_superclass_text`: coarser super-group (for example, 40 → “Forest”).
- The enriched index also provides normalized `land_cover_detail*` and `land_cover_superclass*` aliases.
- These fields enable stratified metrics such as PSNR, SSIM, and SAM by environment type.
- Missing values may occur outside coverage or over NoData areas.

---

## Geospatial & Data Notes
- **CRS:** Tiles use projected UTM zones; the exact CRS and affine transforms are included per record. Centroids are provided in WGS84.
- **Alignment:** Canonical 2.5 m HR is an exact 4× scaling of LR; supplementary 1 m HR is an exact 10× scaling. Both are pixel-grid aligned.
- **Nodata:** LR uses 65535 and HR uses 0 as the GeoTIFF nodata value.
- **Bands:** All raster products contain B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, and B12. B10 is excluded.
- **LR grid:** The LR files preserve observed Sentinel-2 radiometry, but all 12 bands are stored on one common 10 m tile grid. Bands with native 20 or 60 m resolution were sampled onto that grid by the GEE export; separate native-resolution grids are not included.
- **HR availability:** The 2.5 m HR reference is the canonical paper product and default. The aligned 1 m workflow product is provided as a supplementary resource for other potential uses.

---

## Intended Uses
- Benchmarking SR models (classical, CNN, diffusion, and GAN approaches).
- Stratified evaluation by land-cover class.
- Qualitative visualization and error analysis.
- Testing spectral and radiometric consistency across all released Sentinel-2 bands.
- Exploring applications that benefit from the supplementary aligned 1 m workflow product.

## Limitations
- Land-cover labels are window-mode summaries, not per-pixel annotations.
- Some tiles may lack land-cover labels or cross class boundaries.
- Cross-sensor pairs may retain differences caused by temporal separation, atmosphere, illumination, sensor point-spread functions, and residual coregistration.
- Coverage is limited to North American NEON sites and is imbalanced toward natural land-cover classes.
- The published SEN2NEON benchmark and reported metrics use the canonical 2.5 m HR product, not the supplementary 1 m product.

---

## Integrity
`s2_l2a_10m.sha256` records the SHA-256 checksum of every canonical LR file. The corrected files were verified byte-for-byte against the archived original GEE exports before this release was staged.

---

## License
SEN2NEON is distributed under CC BY 4.0. Please retain attribution when redistributing or deriving work from the dataset.

---

## Citation
If you use SEN2NEON in your research, please cite:

```bibtex
@article{donike2026sen2neon,
  author  = {Donike, Simon and Aybar, Cesar and Contreras, Julio and G{\'o}mez-Chova, Luis},
  title   = {SEN2NEON: Enabling Quantitative Benchmarking of Sentinel-2 Superresolution for All Multispectral Bands},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  volume  = {23},
  pages   = {6013905--6013905},
  year    = {2026},
  doi     = {10.1109/LGRS.2026.3703947}
}
```

---

## Contact
- Maintainer: Image Processing Laboratory, University of Valencia, Spain
- Issues and questions: open a discussion at [ESAOpenSR/SEN2NEON](https://github.com/ESAOpenSR/SEN2NEON).