| --- |
| 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. |
|  |
|
|
| --- |
|
|
| ## 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). |
|
|
