A Multimodal Approach to Heritage Preservation in the Context of Climate Change

• URL: http://arxiv.org/abs/2510.14136v1
• Date: Wed, 15 Oct 2025 22:07:57 GMT
• Title: A Multimodal Approach to Heritage Preservation in the Context of Climate Change
• Authors: David Roqui, Adèle Cormier, nistor Grozavu, Ann Bourges,
• Abstract summary: We propose a lightweight multimodal architecture that fuses sensor data (temperature, humidity) with visual imagery to predict severity at heritage sites.<n>On data from Strasbourg Cathedral, our model achieves 76.9% accu- racy, a 43% improvement over standard multimodal architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cultural heritage sites face accelerating degradation due to climate change, yet tradi- tional monitoring relies on unimodal analysis (visual inspection or environmental sen- sors alone) that fails to capture the complex interplay between environmental stres- sors and material deterioration. We propose a lightweight multimodal architecture that fuses sensor data (temperature, humidity) with visual imagery to predict degradation severity at heritage sites. Our approach adapts PerceiverIO with two key innovations: (1) simplified encoders (64D latent space) that prevent overfitting on small datasets (n=37 training samples), and (2) Adaptive Barlow Twins loss that encourages modality complementarity rather than redundancy. On data from Strasbourg Cathedral, our model achieves 76.9% accu- racy, a 43% improvement over standard multimodal architectures (VisualBERT, Trans- former) and 25% over vanilla PerceiverIO. Ablation studies reveal that sensor-only achieves 61.5% while image-only reaches 46.2%, confirming successful multimodal synergy. A systematic hyperparameter study identifies an optimal moderate correlation target ({\tau} =0.3) that balances align- ment and complementarity, achieving 69.2% accuracy compared to other {\tau} values ({\tau} =0.1/0.5/0.7: 53.8%, {\tau} =0.9: 61.5%). This work demonstrates that architectural sim- plicity combined with contrastive regularization enables effective multimodal learning in data-scarce heritage monitoring contexts, providing a foundation for AI-driven con- servation decision support systems.

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