Integration of large vision language models for efficient post-disaster damage assessment and reporting

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 an 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, a 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, DisasTeller can accelerate disaster response activities, reducing human execution time and structuring information flow. Our evaluation shows both benefits and challenges: while DisasTeller streamlines coordination and report generation, errors in early-stage assessments may propagate downstream, highlighting the need for human validation and improved LVLM accuracy. This framework acts as a complementary support system to expert teams, bridging the gap between traditional response methods and emerging LVLM-driven efficiency, while highlighting the importance of continued refinement and collaboration for safe, trustworthy deployment. Disaster response often suffers from delays and inconsistent assessments. Here, the authors develop DisasTeller, a multi-agent LVLM system that interprets disaster images, generates alerts and plans, and provides rapid, standardised support for post-disaster decision-making.