# Gtpbd Eval

> Evaluates fine-grained agricultural parcel delineation, boundary detection, and cross-domain generalization on high-resolution remote sensing imagery of terraced terrain. It benchmarks semantic segmentation, edge extraction, and parcel extraction models across multiple geographic domains. Use when the user wants to benchmark on GTPBD, or asks about evaluating this task. Reports IoU.

- Skill: `qhjqhj00/gtpbd-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/gtpbd-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/gtpbd-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/gtpbd-eval

---


# gtpbd-eval

> GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset — Zhiwei Zhang et al. (2025) (arXiv:2507.14697, 2025)

## What this evaluates

Evaluates fine-grained agricultural parcel delineation, boundary detection, and cross-domain generalization on high-resolution remote sensing imagery of terraced terrain. It benchmarks semantic segmentation, edge extraction, and parcel extraction models across multiple geographic domains.

## Datasets

- **GTPBD** — total 47537; splits: train (-1), val (-1), test (-1); repo https://github.com/Z-ZW-WXQ/GTPBD

## Metrics

- `IoU` **(primary)** — range: [0, 1]
  - Intersection over Union: ratio of the intersection area between predicted and ground truth masks to their union area.
- `F1-score` — range: [0, 1]
  - Harmonic mean of Precision and Recall: 2 * (Prec * Rec) / (Prec + Rec).
- `ODS` — range: [0, 1]
  - Optimal Dataset Scale F1-score: F1 computed using a single threshold optimized over the entire dataset.
- `OIS` — range: [0, 1]
  - Optimal Image Scale F1-score: F1 computed using the best threshold per image, then averaged across the dataset.
- `AP` — range: [0, 1]
  - Average Precision: area under the precision-recall curve across multiple thresholds.
- `GOC` — range: percent
  - Global Over-Classification Error: percentage of parcels incorrectly split or merged, measuring over-segmentation at the object level.
- `GUC` — range: percent
  - Global Under-Classification Error: percentage of parcels missed or merged, measuring under-segmentation at the object level.
- `GTC` — range: percent
  - Global Total Classification Error: sum of GOC and GUC, representing total object-level classification error.

## Input / output format

**Input**: 512×512 cropped patches of high-resolution remote sensing imagery covering terraced agricultural terrain.

**Output**: Pixel-level segmentation masks, edge probability maps (binarized), or object-level parcel masks/polygons.

## Scoring recipe

```python
def compute_iou_f1(pred_mask, gt_mask, thr=0.5):
    pred_bin = (pred_mask >= thr).astype(int)
    inter = np.logical_and(pred_bin, gt_mask).sum()
    union = np.logical_or(pred_bin, gt_mask).sum()
    iou = inter / union if union > 0 else 0.0
    tp = inter
    fp = np.logical_and(pred_bin, ~gt_mask).sum()
    fn = np.logical_and(~pred_bin, gt_mask).sum()
    prec = tp / (tp + fp) if (tp + fp) > 0 else 0
    rec = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
    return iou, f1

def compute_ods(pred_maps, gt_edges):
    # Optimize single threshold over all images to maximize F1
    pass

def compute_ois(pred_maps, gt_edges):
    # Optimize threshold per image, then average F1
    pass
```

## Common pitfalls

- Edge ground truth is synthetically generated via morphological erosion/dilation (1–5 px widths) rather than manually traced boundaries.
- Object-level metrics (GOC/GUC/GTC) measure parcel classification errors (over/under-segmentation) rather than boundary distance errors.
- UDA experiments use specific geographic domain pairs (S, N, G) representing distinct terraced zones, not standard synthetic-to-real splits.

## Evidence (verbatim from paper)

> To evaluate segmentation accuracy on the GTPBD dataset, we adopt five standard pixel-level metrics commonly used in semantic segmentation and unsupervised domain adaptation (UDA) tasks, including Precision (Prec.), Recall (Rec.), Intersection over Union (IoU), Overall Accuracy (OA) and F1-score;

## Citation

```bibtex
@misc{zhang2025gtpbd,
  title={GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset},
  author={Zhiwei Zhang et al. (2025)},
  year={2025},
  note={arXiv:2507.14697}
}
```

- arXiv: 2507.14697