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README.md

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+---
+license: other
+license_name: "usgs-public-domain"
+pretty_name: "SeeFar V0 - Landsat"
+size_categories:
+- 10K<n<100K
+task_categories:
+- image-feature-extraction
+tags:
+- earth-observation
+- remote-sensing
+- taco
+---
+
+<p align="center"><img src="assets/logos.png" width="546" alt="Image and Signal Processing Group, Universitat de València · ELLIOT"></p>
+
+# SeeFar V0 - Landsat
+
+> **This is a repackaging, not a new dataset.** It is **SeeFar V0 - Landsat** by USGS / NASA (Landsat 8/9), Coastal Carbon (SeeFar assembly; Lowman et al.), converted to TACO. Pixel values and labels are kept as released except where the description below says otherwise. All credit belongs to the original authors: if you use it, please cite them and follow their licence.
+>
+> [original dataset](https://registry.opendata.aws/seefar/) · [paper](https://doi.org/10.48550/arXiv.2406.06776) · licence: USGS public domain (Landsat Data Distribution Policy)
+>
+> Repackaged into TACO by the [Image and Signal Processing Group (ISP)](https://isp.uv.es/), Universitat de València, within the [ELLIOT](https://elliot-ai.eu) project.
+
+## Citation
+
+Please cite the original work:
+
+```bibtex
+@article{lowman2024seefar,
+  title   = {SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models},
+  author  = {Lowman, James and Zheng, Kelly Liu and Fraser, Roydon and The, Jesse Van Griensven and Valipour, Mojtaba},
+  journal = {arXiv preprint arXiv:2406.06776},
+  year    = {2024}
+}
+```
+
+## About the data
+
+The landsat half of SeeFar V0: 33282 four-band (blue, green, red, NIR) tiles from Landsat 8/9 OLI at 30 m. No labels -- this is a multi-resolution self-supervised pretraining corpus.
+
+**33,282 samples** · splits: `test` 4,991 · `train` 23,307 · `validation` 4,984 · tasks: `similarity-search`
+
+Packaged as [TACO v3](https://asterisk.coop/taco/spec/).
+
+<details>
+<summary>Full description</summary>
+
+**Splits.** The release's own: 23307 train, 4991 test, 4984 val. The release spells the last one `validate`; the partition is untouched and `split_original` keeps its word.
+
+**Scope.** SeeFar ships four sources in one archive and they are not co-registered: the four id spaces are independent, the four-way filename intersection is 337 of 23,527 in train and 1 in test, and for 120 of those 337 the closest same-name tile in another source is 546 km away, with 0 of 360 pairs within 10 km. Each source is therefore its own collection and nothing may be joined across them; `shared_stem` records the filename coincidence.
+
+**Geometry.** Tiles are in per-tile UTM zones, so `resolution_m` is measured geodesically per sample. The band table is the release's own per-tile wavelength tags, asserted on every file.
+
+**Radiometry.** Collection-2 Level-2 surface reflectance, `0.0000275 * DN - 0.2`, exactly. Three measurements establish that rather than assume it:
+- the corpus maximum over a 150-tile sample is 65,454, which is the C2 L2 valid ceiling;
+- the release's own wavelength tags reproduce OLI's B2-B5 to the nanometre;
+- reduced, the 40 most-vegetated tiles average 0.024 blue, 0.047 green, 0.035 red and 0.316 NIR, which is what a green canopy does.
+
+**Exclusions.** 329 members of this source are truncated in the release -- 33 distinct shapes, none 384x384, and not one holding a single non-zero pixel over a full scan of all 329 -- and are excluded by name rather than repaired. The counts above are after that exclusion.
+
+</details>
+
+## Getting started
+
+```bash
+git clone https://github.com/OscarPellicer/taco
+pip install -e "taco/python[ml]"     # builds the native reader: needs CMake and Ninja
+```
+
+Read it straight from the Hub:
+
+```python
+from huggingface_hub import snapshot_download
+from taco.ml import Dataset, plot_sample
+
+path = snapshot_download("isp-uv-es/seefar-landsat-taco", repo_type="dataset")
+ds = Dataset(path)                  # the repository is the container: parts + .tacocat
+plot_sample(ds[0])
+```
+
+or from a local copy:
+
+```python
+ds = Dataset("seefar-landsat")
+sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
+sample["image"].array.shape
+```
+
+Metadata without decoding anything:
+
+```python
+import taco
+taco.read("seefar-landsat/.tacocat")              # one Arrow table, levels joined
+```
+
+## Samples
+
+![sample](assets/sample_0.png)
+![sample](assets/sample_1.png)
+![sample](assets/sample_2.png)
+![sample](assets/sample_3.png)
+![sample](assets/sample_4.png)
+
+## What a sample contains
+
+| role | slot | holds | modality | detail |
+|---|---|---|---|---|
+| input | `image` | raster | optical | 4 band(s), unit 1, scaled |
+
+## Licence
+
+USGS public domain (Landsat Data Distribution Policy)
+
+Providers: USGS / NASA (Landsat 8/9), Coastal Carbon (SeeFar assembly; Lowman et al.)
+
+## Acknowledgements
+
+TACO was designed by **César Aybar** and is specified at [https://asterisk.coop/taco/spec/](https://asterisk.coop/taco/spec/).
+
+Built by [Oscar Pellicer](https://scholar.google.com/citations?hl=es&user=CCFJshwAAAAJ&view_op=list_works&sortby=pubdate) within the [ELLIOT](https://elliot-ai.eu) project at the
+[Image and Signal Processing Group (ISP)](https://isp.uv.es/), Universitat de València.