Integration of Large Vision Language Models for Efficient Post-disaster Damage Assessment and Reporting
This addresses the challenge of delayed disaster management for response teams and affected communities, particularly in underdeveloped regions, though it appears incremental as it builds on existing LVLM technology.
The paper tackles the problem of slow and inefficient post-disaster response by introducing DisasTeller, a multi-agent framework using Large Vision Language Models to automate tasks like damage assessment and resource allocation, which reduces human execution time and optimizes resource distribution.
Traditional natural disaster response involves significant coordinated teamwork where speed and efficiency are key. Nonetheless, human limitations can delay critical actions and inadvertently increase human and economic losses. Agentic Large Vision Language Models (LVLMs) offer a new avenue to address this challenge, with the potential for substantial socio-economic impact, particularly by improving resilience and resource access in underdeveloped regions. We introduce DisasTeller, the first multi-LVLM-powered framework designed to automate tasks in post-disaster management, including on-site assessment, emergency alerts, resource allocation, and recovery planning. By coordinating four specialised LVLM agents with GPT-4 as the core model, DisasTeller autonomously implements disaster response activities, reducing human execution time and optimising resource distribution. Our evaluations through both LVLMs and humans demonstrate DisasTeller's effectiveness in streamlining disaster response. This framework not only supports expert teams but also simplifies access to disaster management processes for non-experts, bridging the gap between traditional response methods and LVLM-driven efficiency.