DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Public Safety & Security · Depth: Expert, extended

Summary

DisasterBench is a new multi-stage multimodal reasoning benchmark designed for UAV-based disaster response in complex environments. It features 5,330 real-world low-altitude UAV images and 29,300 reasoning-oriented samples, covering 14 disaster-related scene types and 9 response-critical tasks across pre-, during-, and post-disaster stages. The benchmark explicitly tests causal attribution, propagation prediction, damage analysis, and decision-oriented reasoning. Alongside this, DisasterVL, a lightweight 2B-parameter multimodal model, is introduced. Optimized with a three-stage pipeline (domain instruction tuning, chain-of-thought-guided multimodal alignment, and reinforcement learning-based policy optimization), DisasterVL achieves 72.60% overall accuracy on the test set, outperforming all 21 evaluated open-source models and narrowing the gap to closed-source models like GPT-4o with superior efficiency (168 average tokens generated).

Key takeaway

For AI Scientists and Machine Learning Engineers developing edge-deployed disaster response systems, DisasterBench offers a robust evaluation standard for multi-stage reasoning. You should consider adopting the three-stage training framework demonstrated by DisasterVL to enhance lightweight multimodal models. This approach significantly improves accuracy and token efficiency, making advanced reasoning capabilities practical for on-site UAV operations with limited compute resources.

Key insights

Multi-stage multimodal reasoning is crucial for effective UAV-based disaster response, requiring specialized benchmarks and lightweight models.

Principles

• Disaster analysis requires multi-stage reasoning, not isolated perception.
• Lightweight models benefit from progressive, domain-aware training.
• Visual evidence is critical for accurate disaster reasoning.

Method

DisasterVL's three-stage training pipeline involves domain knowledge injection, chain-of-thought-guided multimodal alignment, and policy-based refinement for robust, efficient reasoning.

In practice

• Use DisasterBench to evaluate multi-stage disaster reasoning.
• Apply the three-stage training for lightweight MLLM optimization.

Topics

• Multimodal Reasoning
• UAV-Based Disaster Response
• Vision-Language Benchmarks
• Edge AI
• Model Optimization
• Emergency Response Intelligence

Code references

• TanmouTT/DisasterBench

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.