# Long Term Vpr Eval

> Evaluates long-term visual place recognition (VPR) capabilities in dynamic underwater benthic environments. It probes a model's ability to geolocate camera views over multi-year intervals despite habitat changes, varying terrain ruggedness, and sub-decimeter registration errors. Use when the user wants to benchmark on Benthic Reference Sites Dataset, or asks about evaluating this task. Reports Recall@K.

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

---


# long-term-vpr-eval

> Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark — Larsen et al. (2026) (arXiv:2603.04056, 2026)

## What this evaluates

Evaluates long-term visual place recognition (VPR) capabilities in dynamic underwater benthic environments. It probes a model's ability to geolocate camera views over multi-year intervals despite habitat changes, varying terrain ruggedness, and sub-decimeter registration errors.

## Datasets

- **Benthic Reference Sites Dataset** — total ?; splits: benchmark (-1)

## Metrics

- `Recall@K` **(primary)** — range: percent
  - Recall@K measures the fraction of query images for which at least one ground-truth matching database image appears in the top-K retrieved results. Ground-truth matches are defined by a footprint-based IoU threshold of τ_f ≥ 0.07.

## Input / output format

**Input**: Stereo camera images (query and database views) from different survey visits over benthic sites, along with camera pose/calibration data for footprint computation.

**Output**: Ranked list of database images retrieved for each query image.

## Scoring recipe

```python
def compute_recall_at_k(queries, database, top_k, iou_thresh=0.07):
    hits = 0
    for q in queries:
        retrieved = get_top_k_retrievals(q, database, top_k)
        if any(compute_footprint_iou(q, db) >= iou_thresh for db in retrieved):
            hits += 1
    return (hits / len(queries)) * 100
```

## Common pitfalls

- Using traditional location-based ground truth overestimates VPR performance in rugged terrain; the paper validates footprint-based matching as superior.
- Ignoring residual registration errors between multi-year visits can cause false positives; a conservative footprint IoU threshold (τ_f ≈ 0.07) is required.
- Performance is highly dependent on seafloor type and revisit interval, so averaging across all sites without stratification hides critical model weaknesses.

## Evidence (verbatim from paper)

> To assess how the selected VPR models generalize across seafloor types, we evaluate their mean Recall@K across all visit pairs for each reference site. This provides insight into how seafloor characteristics influence localization performance. As shown in Figure 7, we report the mean Recall@K over a range of rank cutoffs K, illustrating how retrieval accuracy varies with the number of candidates considered. Ground-truth correspondences are defined using footprint-based camera links established through the methodology in Section 2.5.2, applying the lower footprint IoU threshold (τ_f=0.07) derived in Section 3.2 to compensate for residual registration errors between visits.

## Citation

```bibtex
@misc{larsen2026longterm,
  title={Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark},
  author={Larsen et al. (2026)},
  year={2026},
  note={arXiv:2603.04056}
}
```

- arXiv: 2603.04056