Fujiview Svf Eval

fujiview-svf-eval

FujiView: Multimodal Late-Fusion for Predicting Scenic Visibility — Bible et al. (2026) (arXiv:2603.00157, 2026)

What this evaluates

Evaluates multimodal late-fusion models for predicting scenic visibility (clear, cloudy, perfect, obscured) across short- to medium-term forecasting horizons (+0d to +3d). It probes the model's ability to integrate visual webcam features with meteorological forecasts to handle class imbalance and temporal dynamics in environmental perception.

Datasets

• FujiView — total 100000; splits: train (-1), val (-1), test (-1)

Metrics

• accuracy (ACC) (primary) — range: percent
  • Percentage of correct predictions out of total instances. Computed as the proportion of samples where the predicted visibility class matches the ground truth label.
• AUC — range: [0, 1]
  • Area under the Receiver Operating Characteristic curve, measuring the model's ability to rank positive instances higher than negative ones across all classification thresholds.

Input / output format

Input: Concatenated feature vector containing calibrated softmax probabilities from a YOLOv8n-cls vision model (trained on webcam images) and meteorological features (current conditions and forecasts up to 3 days ahead).

Output: Predicted visibility class label (OBSCURED, PERFECT, CLEAR, or CLOUDY).

Scoring recipe

def compute_metrics(predictions, labels, probabilities):
    acc = sum(1 for p, l in zip(predictions, labels) if p == l) / len(labels)
    auc = roc_auc_score(labels, probabilities, multi_class='ovr')
    return acc, auc

Common pitfalls

• Class imbalance heavily favors the OBSCURED class, which can bias models toward predicting non-visibility if not handled.
• Horizon difficulty is non-monotonic; +1d forecasting can be easier than +0d due to synoptic weather stability vs. transient cloud dynamics.
• Temporal windowing (averaging frames over 3 hours) improves vision-only AUC but may oversmooth features and reduce accuracy for fusion models at longer horizons.

Evidence (verbatim from paper)

Table 1 summarizes mean accuracy (ACC)—the percentage of correct predictions—and area under the ROC curve (AUC)—a threshold-independent measure of ranking performance—computed from 5-fold grouped cross-validation.

Citation

@misc{bible2026fujiview,
  title={FujiView: Multimodal Late-Fusion for Predicting Scenic Visibility},
  author={Bible et al. (2026)},
  year={2026},
  note={arXiv:2603.00157}
}

• arXiv: 2603.00157