Explainable flood damage assessment using multi-atrous self-attention and vision-language integration

Flood disasters triggered by excessive rainfall cause severe damage to infrastructure and pose significant risks to human life. Within the context of disaster management, accurately identifying affected structures and providing interpretable analytical results are of critical importance. This study proposes a new disaster analysis framework that integrates the Multi-Atrous Self-Attention (MASA) mechanism, which is designed to capture multi-scale spatial features effectively, with vision-language models for explainable flood assessment. The proposed approach consists of two main components. The first component performs segmentation to detect and quantify flood-affected structures, while the second component employs a fine-tuned vision language model to generate natural language descriptions of the disaster scene. The MASA module processes image-mask pairs from the FloodNet dataset to segment disaster related structures, whereas the LoRA (Low Rank Adaptation) enhanced BLIP-2 (Bootstrapped Language Image Pre-training) model learns image-text pairs from the LADI-v2 dataset to produce textual disaster descriptions. Through this dual stage structure, the system provides both quantitative and linguistic outputs, enabling interpretable flood impact assessment. Experimental results demonstrate that the proposed MASA-based segmentation model achieves a mean Intersection over Union (mIoU) of 73.78% on FloodNet, outperforming state-of-the-art segmentation models. Furthermore, the LoRA-fine-tuned BLIP-2 model achieves a BLEU score of 80.77% on the LADI-v2 dataset, indicating fluent, contextually relevant, and semantically coherent textual outputs. The proposed system contributes to disaster analysis by enhancing explainability and interpretability in flood damage assessment. • A novel dual-stage framework integrates segmentation and vision-language modeling. • The MASA module enhances flood segmentation via multi-scale self-attention fusion. • The model achieves 73.78 % mIoU on FloodNet, outperforming UNet, LinkNet and DeepLabV3+. • BLIP-2 + LoRA generates context-aware disaster scene descriptions with 80.77 % BLEU. • Provides interpretable flood damage assessment combining quantitative and linguistic insights.