NA-SAR

NA-SAR is a North America synthetic aperture radar pretraining dataset built from the NASA OPERA archive. It is intended for self-supervised and masked-image pretraining of SAR foundation models.

This release is an unsplit pretraining corpus. There are no train/validation/test partitions because the dataset is not intended to define an evaluation protocol. Downstream evaluations should define their own geographically and temporally appropriate splits.

Dataset Contents

• Samples: 1,099,604
• Patch size: 128 x 128 pixels
• RTC views: two spatial views per sample
• Temporal RTC acquisitions: prime and secondary
• RTC channels: co-pol and cross-pol, with missing polarization zero padded
• Incidence angle: one channel per spatial view
• InSAR phase: stored as cos(phi), sin(phi)
• Coherence: one channel per spatial view

Each tensor sample contains:

Key	Shape	Description
prime_rtc_view_0	(2, 128, 128)	Prime RTC view 0, co-pol/cross-pol
secondary_rtc_view_0	(2, 128, 128)	Secondary RTC view 0, co-pol/cross-pol
prime_rtc_view_1	(2, 128, 128)	Prime RTC view 1, co-pol/cross-pol
secondary_rtc_view_1	(2, 128, 128)	Secondary RTC view 1, co-pol/cross-pol
inc_angle_view_0	(1, 128, 128)	Incidence angle for view 0
inc_angle_view_1	(1, 128, 128)	Incidence angle for view 1
ifg_view_0	(2, 128, 128)	InSAR phase for view 0 as cos/sin
coh_view_0	(1, 128, 128)	Coherence for view 0
ifg_view_1	(2, 128, 128)	InSAR phase for view 1 as cos/sin
coh_view_1	(1, 128, 128)	Coherence for view 1

Metadata

metadata_hf.parquet is the publishable metadata table for the sharded release. It includes OPERA provenance, patch geometry, quality score, polarization availability, and the following sharding columns:

• sample_key: sample key inside the WebDataset shard
• shard_path: relative path to the tar shard
• shard_index: integer shard id
• sample_index_in_shard: row order within that shard

The metadata is filtered to samples with quality_score > 0.53.

Loading With Hugging Face Datasets

The sharded release can be streamed with the WebDataset loader:

from io import BytesIO

import numpy as np
from datasets import load_dataset

ds = load_dataset(
    "webdataset",
    data_files={"train": "data/nasar-train-*.tar"},
    split="train",
    streaming=True,
)

sample = next(iter(ds))
arrays = np.load(BytesIO(sample["npz"]))
print(arrays.files)

Loading With PyTorch

The repository includes a lightweight PyTorch helper:

from nasar_dataset import NASARRTCDataset, NASARInSARDataset

rtc_ds = NASARRTCDataset("/path/to/NA-SAR")
insar_ds = NASARInSARDataset("/path/to/NA-SAR")

For streaming at scale, use the WebDataset tar shards under data/. Each tar member is a compressed .npz tensor file named {sample_key}.npz.

Preprocessing Notes

RTC arrays are stored as raw linear backscatter with invalid, non-finite, and negative values set to zero. The helper loader applies the pretraining-time RTC transform by default:

x = log1p(20.0 * x)
x = clip_and_rescale(x, p1=0.14, p99=2.38)

InSAR phase is stored as cosine and sine channels instead of wrapped phase radians to avoid phase discontinuities at the wrap boundary.

Source and Scope

The source data comes from OPERA SAR products over North America. Users should follow the applicable terms and citation guidance for OPERA source products.

Intended Use

This dataset is intended for SAR and InSAR representation learning, especially self-supervised pretraining. It is not an evaluation benchmark by itself.

Limitations

• Coverage is geographically focused on North America.
• Quality filtering removes lower-quality samples and can bias the corpus toward easier or cleaner acquisitions.
• Missing RTC polarizations are represented by zero-padded channels. The metadata includes polarization availability flags so users can distinguish true zeros from missing channels.