Datasets:
metadata
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.tarshards. Each sample keyKhas 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 canseek+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/255.0gives normalized [0,1]. - SAR: linear amplitude → dB, clipped to
[-40, 5]→[0,1]→uint8. Decode:x/255.0gives 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_hoursin metadata).