| --- |
| 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). |
|
|
