README.md

---
license: other
pretty_name: Sentinel-1 GRD + Land Cover
tags:
- sar
- sentinel-1
- land-cover
- esa-worldcover
- remote-sensing
- webdataset
task_categories:
- image-segmentation
- image-to-image
size_categories:
- 10K<n<100K
---

# ONERA/S1_GRD_LC — Sentinel-1 GRD + Land Cover

For each high-resolution SAR imagette (UMBRA) from SARLO-80 dataset, we provide the
co-located **Sentinel-1 GRD 10 m** backscatter (VV/VH) and the corresponding
**ESA WorldCover 10 m** land-cover map.


![preview](preview_00000000.png)



## Source

Each sample is anchored on a high-resolution **UMBRA SAR imagette**. From the
SICD metadata we recover the imagette's **4 WGS84 corners** (image-to-ground
projection) and derive its geographic bounding box.

## Adding Sentinel-1 GRD (method + format)

**Method.** From the 4 corners we build a north-up **UTM grid at 10.0 m**.
We then query **Sentinel-1 GRD** (IW mode, **VV + VH**) from the
**Copernicus Data Space Ecosystem** through the *Sentinel Hub Process API*,
sampled directly on that grid. Backscatter is **orthorectified**
(Copernicus DEM), expressed as `GAMMA0_TERRAIN`, and converted to **dB**.
The composite is built over `2020-01-01 → 2021-01-01`. 

Because the S1 GRD is orthorectified (north-up product), there is **no** warp
to the SAR geometry at this stage: S1 and land cover share the same grid
(`crs` + `transform` + dimensions) and are therefore **co-registered**.

**Format.**

| File | Type | Description |
|---|---|---|
| `{id}.map.s1_grd.npy` | float32 `(2, H, W)` | [VV, VH] in dB, UTM grid |
| `{id}.map.s1_vv.png` / `s1_vh.png` | uint8 `(H, W)` | dB visualization (VV∈[-25,0], VH∈[-30,-5]) |
| `{id}.map.s1_rgb.png` | uint8 `(H, W, 3)` | RGB = [VV, VH, VV−VH] |

Collection: `SENTINEL1_IW`. Units of the `.npy`: **dB**. PNGs are simple 8-bit
renderings with a fixed dynamic range (for visual inspection only).

## Land cover

Source: **ESA WorldCover v100 (2020)**, sampled on the **same grid** as the S1 GRD.

| File | Type | Description |
|---|---|---|
| `{id}.map.landcover.npy` | uint8 `(H, W)` | WorldCover class IDs |
| `{id}.map.landcover.png` | uint8 `(H, W, 3)` | official colorization |

Class table:

| ID | Class | Color |
|---|---|---|
| 10 | Tree cover | `#006400` |
| 20 | Shrubland | `#ffbb22` |
| 30 | Grassland | `#ffff4c` |
| 40 | Cropland | `#f096ff` |
| 50 | Built-up | `#fa0000` |
| 60 | Bare / sparse vegetation | `#b4b4b4` |
| 70 | Snow and ice | `#f0f0f0` |
| 80 | Permanent water bodies | `#0064c8` |
| 90 | Herbaceous wetland | `#0096a0` |
| 95 | Mangroves | `#00cf75` |
| 100 | Moss and lichen | `#fae6a0` |

## Without / with affine transform

Every sample is available in **two co-registered frames**:

- **`map_grid`** (prefix `map.`) — north-up **UTM 10.0 m** map grid,
  **without** any affine transform.
- **`sar_frame`** (prefix `sar.`) — the same S1 and land cover
  **resampled into the native UMBRA SAR image frame** using an **affine
  transform** estimated from the 4 corners (S1 bilinear, land cover
  nearest-neighbor). Aligned with the original SAR amplitude
  (`{id}.umbra_sar.png`). The 2×3 affine matrix is stored in `meta.json`
  (`affine_map_to_sar_2x3`).


## Sample contents

```
{id}.map.s1_grd.npy       {id}.sar.s1_grd.npy
{id}.map.s1_vv.png        {id}.sar.s1_vv.png
{id}.map.s1_vh.png        {id}.sar.s1_vh.png
{id}.map.s1_rgb.png       {id}.sar.s1_rgb.png
{id}.map.landcover.npy    {id}.sar.landcover.npy
{id}.map.landcover.png    {id}.sar.landcover.png
{id}.umbra_sar.png        (original UMBRA SAR amplitude, SAR frame)
{id}.meta.json            (corners, bbox, grid, affine, S1/LC info)
```

## Directory structure

```
train/chunk_XXX/shard-YYYYY.tar   (WebDataset)
```

## Loading (WebDataset)

```python
import webdataset as wds, numpy as np, io, json

url = "https://huggingface.co/datasets/ONERA/S1_GRD_LC/resolve/main/train/chunk_000/shard-00000.tar"
ds = wds.WebDataset(url)
for s in ds:
    vv_vh = np.load(io.BytesIO(s["map.s1_grd.npy"]))     # (2,H,W) dB
    lc    = np.load(io.BytesIO(s["map.landcover.npy"]))  # (H,W) classes
    meta  = json.loads(s["meta.json"])
    break
```

---
*`map_grid` = without affine (UTM), `sar_frame` = with affine (SAR frame).*