Integrated Multimodal AI System for Retrieval-Augmented Reasoning, Object Sensing, and Damage Analysis

2026-08-09

AI Summary

This work addresses the challenges of reliable damage assessment under adverse lighting and weather conditions, where conventional vision-based methods often fail and language models are prone to hallucination due to insufficient grounding in domain-specific documentation. To overcome these limitations, the authors propose a unified multimodal AI system that integrates retrieval-augmented generation (RAG), knowledge graphs, thermal-infrared and visible-light imaging, and wireless signal sensing. A novel hybrid retrieval mechanism combining graph-structured and vector-based representations is introduced to enhance cross-document reasoning. Additionally, a vision-language model generates synthetic damage data to augment training. Experimental results demonstrate that dynamic retrieval significantly improves factual consistency, graph-based retrieval outperforms purely vector-based approaches, and multimodal fusion effectively mitigates the constraints of individual sensors, collectively enhancing damage classification accuracy.

Abstract

This work presents a unified multimodal AI system for damage assessment that integrates retrieval-augmented generation (RAG) models, thermal spectrum perception, vision foundation model pipelines, and exploratory wireless signal sensing. A RAG component is developed to ground a locally hosted language model in project-specific documentation, including specialized damage level classification criteria to mitigate hallucinations during inference. Controlled comparisons against static few-shot prompting demonstrate that dynamic retrieval improves grounding and factual consistency. We further compare vector-based RAG with a knowledge graph variant constructed via entity-relation extraction, and show that graph-based retrieval produces stronger responses for damage assessment queries requiring cross-document reasoning, motivating hybrid dense, sparse, and graph-aware retrieval. To address limitations of EO imagery under adverse lighting and weather conditions, infrared (IR)/thermal sensing is employed for object detection and segmentation. Our detectors generate candidate detections, yielding improved segmentation of a broad array of objects. Paired IR versus visible spectrum tracking experiments reveal failure modes, motivating multimodal fusion for robust object detection and damage analysis. Vision foundation and vision-language models are leveraged to generate synthetic damage imagery and classify damage severity with high accuracy, supporting training and validation of downstream damage assessment models. Finally, exploratory Wireless-based sensing demonstrates potential to detect presence, motion, and post-event environmental changes where EO and IR sensing are ineffective.

Problem

• damage assessment
• multimodal sensing
• retrieval-augmented reasoning
• object detection
• environmental robustness

Innovation

• Retrieval-Augmented Generation
• Multimodal Fusion
• Thermal Sensing
• Knowledge Graph
• Wireless Sensing