TriDA: Privacy-Aware and Efficient Multimodal AI for Disaster Assessment

As disaster imagery and social media reports become vital for crisis response, automated assessment systems must address challenges of multimodal integration, privacy-aware learning, and computational efficiency. To address these challenges, we propose TriDA, a privacy-aware and efficiency-conscious multimodal disaster classification framework that fuses image features with text representations through a late-fusion design. A classifier-head DP-SGD stage is used to report training-record-level differential privacy accounting for paired image–text samples under the stated private optimization protocol. To study efficiency-oriented simplification, structured neuron pruning reduces redundant capacity in the classification head while preserving predictive utility. Experiments on the multimodal damage identification dataset show that TriDA maintains strong classification performance, exhibits a controlled privacy–utility trade-off under increasing DP noise, and achieves quantifiable classifier-head parameter and MAC reductions through pruning. These findings position TriDA as a controlled empirical framework for privacy-aware and resource-conscious multimodal disaster assessment.