# Pastis Hd Eval

> Tests agricultural land cover mapping and crop-type classification by evaluating models on high-resolution satellite imagery combined with optical and radar time series. Use when the user wants to benchmark on PASTIS-HD, or asks about evaluating this task. Reports macro-averaged F1-score.

- Skill: `qhjqhj00/pastis-hd-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/pastis-hd-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/pastis-hd-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/pastis-hd-eval

---


# pastis-hd-eval

> OmniSat: Self-Supervised Modality Fusion for Earth Observation — Astruc et al. (2024) (arXiv:2404.08351, 2024)

## What this evaluates

Tests agricultural land cover mapping and crop-type classification by evaluating models on high-resolution satellite imagery combined with optical and radar time series.

## Datasets

- **PASTIS-HD** — total 2433; splits: train (-1), test (-1); repo https://github.com/gastruc/OmniSat

## Metrics

- `macro-averaged F1-score` **(primary)** — range: percent
  - Unweighted mean of per-class F1 scores, treating all 18 crop types equally regardless of class frequency.

## Input / output format

**Input**: Satellite patches of 1280x1280m containing Sentinel-2 optical time series, Sentinel-1 radar time series, and contemporary VHR SPOT 6-7 images.

**Output**: Multi-class classification label indicating one of 18 crop types per patch.

## Scoring recipe

```python
def compute_macro_f1(gold, pred):
    from sklearn.metrics import f1_score
    return f1_score(gold, pred, average='macro', zero_division=0) * 100
```

## Common pitfalls

- Ignoring the irregular cloud occlusion in optical time series, which requires robust date-filtering or masking.
- Evaluating on single-date images instead of the full time series, missing critical temporal dynamics.

## Evidence (verbatim from paper)

> We report the macro-averaged F1-score for crop-type multi-class classification on the PASTIS-HD dataset.

## Citation

```bibtex
@misc{astruc2024omnisat,
  title={OmniSat: Self-Supervised Modality Fusion for Earth Observation},
  author={Astruc et al. (2024)},
  year={2024},
  note={arXiv:2404.08351}
}
```

- arXiv: 2404.08351