| --- |
| viewer: false |
| 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) |
| co_pol = clip_and_rescale(co_pol, p1=0.15, p99=2.39) |
| cross_pol = clip_and_rescale(cross_pol, p1=0.01, p99=1.09) |
| ``` |
|
|
| The co-pol and cross-pol RTC channels use separate normalization ranges because |
| cross-pol backscatter is substantially darker. Fully missing polarizations remain |
| zero-padded after preprocessing. |
|
|
| 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. |
|
|
