---
license: cc-by-4.0
task_categories:
- image-feature-extraction
tags:
- remote-sensing
- sar
- optical
- multimodal
- contrastive-learning
- sentinel-1
- landsat
size_categories:
- 1M<n<10M
---

# VIS–SAR Satellite Dataset

Spatially- and temporally-aligned **visible (optical) ↔ SAR** satellite image patches
for cross-modal representation learning (CLIP-style SAR↔visible alignment).

## Contents

- **~3.68M paired patches** (this is a snapshot of an in-progress 10M build).
- **`shards/`** — WebDataset `.tar` shards. Each sample key `K` has three members:
  - `K.vis.npy` — visible/optical, **`uint8 [3, 224, 224]`** (3 bands)
  - `K.sar.npy` — SAR, **`uint8 [2, 224, 224]`** (VV, VH)
  - `K.json` — per-sample metadata (AOI, biome, region, lat/lon, acquisition times, CRS, transform, SAR scaling, cloud cover, Δt hours)
- **`index.parquet`** — random-access offset index, one row per sample:
  `key, shard, vis_offset, vis_size, sar_offset, sar_size, ground_tile_id, aoi_id, biome, region`.
  A worker can `seek`+read a single sample without scanning the shard.
- **`manifests/`** — per-shard metadata sidecars. **`manifest.parquet`** — consolidated metadata.

## Decoding (uint8 → physical)

Values are quantized from normalized ranges to `uint8` (4× smaller than float32, retrieval-neutral):

- **Visible**: physical reflectance clipped to `[0, 30000]` → `[0,1]` → `uint8`. Decode: `x/255.0` gives normalized [0,1].
- **SAR**: linear amplitude → dB, clipped to `[-40, 5]` → `[0,1]` → `uint8`. Decode: `x/255.0` gives normalized [0,1]; `dB = x/255*45 - 40`.

## Training

Directly consumable by the multi-modal CLIP training pipeline: point it at the
shard directory and the offset index —

```
--shards_dir  <download>/shards
--index_path  <download>/index.parquet
```

The loader (`tar_dataset`) auto-detects the `uint8` dtype and rescales to `[0,1]`
on read (no re-normalization needed). Use `ground_tile_id` for **leak-free
train/val splits** (group by tile so the same ground location never spans splits).

## Notes

- Patches are 224×224 at a common spatial grid (VIS and SAR resampled to the same grid).
- SAR penetrates cloud/night, so VIS↔SAR pairs are matched within a Δt tolerance (see `dt_hours` in metadata).