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README.md

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----
-license: apache-2.0
----
+---
+pretty_name: "NA-SAR"
+tags:
+- geospatial
+- remote-sensing
+- sar
+- earth-observation
+- self-supervised-learning
+- pretraining
+- webdataset
+- pytorch
+size_categories:
+- 1M<n<10M
+task_categories:
+- image-feature-extraction
+---
+
+# 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:
+
+```python
+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:
+
+```python
+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:
+
+```python
+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.