Gtpbd Mm Eval

gtpbd-mm-eval

GTPBD-MM: A Global Terraced Parcel and Boundary Dataset with Multi-Modality — Zhang et al. (2026) (arXiv:2604.12315, 2026)

What this evaluates

Evaluates multimodal terraced parcel extraction by measuring pixel-level segmentation accuracy, edge-level boundary recovery, and object-level structural consistency across image-only, image+text, and image+text+DEM input settings.

Datasets

• GTPBD-MM — total ?; splits: (unstated); repo https://github.com/Z-ZW-WXQ/GTPBD-MM

Metrics

• mIoU (primary) — range: [0, 1]
  • Mean Intersection over Union. Computed as the average of IoU (intersection over union) across all classes or instances. IoU = TP / (TP + FP + FN).
• F1 — range: [0, 1]
  • Harmonic mean of precision and recall. F1 = 2 * (Precision * Recall) / (Precision + Recall). Evaluates balance between false positives and false negatives.
• ODS — range: [0, 1]
  • Optimal Dataset Scale boundary metric. Measures boundary recovery quality by aggregating precision and recall across all images at the optimal threshold.
• GTC — range: [0, 1]
  • Geometric Total Consistency. Object-level metric reflecting structural consistency and geometric error at the instance level, penalizing merged or fragmented parcels.

Input / output format

Input: High-resolution optical imagery, optional text descriptions, and optional Digital Elevation Model (DEM) data.

Output: Binary or multi-class segmentation mask delineating terraced parcels.

Scoring recipe

def compute_metrics(pred_mask, gt_mask):
    tp = (pred_mask & gt_mask).sum()
    fp = (pred_mask & ~gt_mask).sum()
    fn = (~pred_mask & gt_mask).sum()
    recall = tp / (tp + fn + 1e-6)
    precision = tp / (tp + fp + 1e-6)
    f1 = 2 * precision * recall / (precision + recall + 1e-6)
    iou = tp / (tp + fp + fn + 1e-6)
    # OIS/ODS require contour extraction & threshold sweep
    # GOC/GUC/GTC require instance matching & geometric error calculation
    return {'mIoU': iou, 'F1': f1, 'Recall': recall}

Common pitfalls

• Metrics must be reported separately for each modality setting (Image-only, Image+Text, Image+Text+DEM) rather than aggregated.
• Object-level metrics (GOC, GUC, GTC) require instance-level matching and geometric error calculation, not just pixel-wise comparison.
• Edge metrics (OIS, ODS) depend on specific boundary extraction thresholds and contour matching tolerances detailed in Appendix C.

Evidence (verbatim from paper)

We adopt a three-level evaluation protocol. Pixel-level metrics include Recall, F1, OA, mIoU, and mAcc, which evaluate region-level segmentation quality. Edge-level metrics include OIS and ODS, which measure boundary recovery quality. Object-level metrics include GOC, GUC, and GTC, which reflect geometric error and structural consistency at the object level.

Citation

@misc{zhang2026gtpbdmm,
  title={GTPBD-MM: A Global Terraced Parcel and Boundary Dataset with Multi-Modality},
  author={Zhang et al. (2026)},
  year={2026},
  note={arXiv:2604.12315}
}

• arXiv: 2604.12315