Natural disasters pose a constant threat to urban populations, causing severe risks to human safety, infrastructure, and the environment. The United States experiences approximately 50 major disasters annually, including hurricanes, wildfires, and floods, often leading to significant challenges in emergency management. Traditional response systems face limitations such as information overload and coordination issues, especially in densely populated areas. To address these challenges, this article explores the integration of Artificial Intelligence (AI) and GIScience to enhance real-time decision-making during crises. This work introduces a conversational virtual assistant based on ChatGPT-4o, designed to process real-time geospatial data and assist citizens during emergencies. The system utilizes Large Language Models (LLMs) and geospatial resources to access, synthesize, and analyze official sources, generate customized evacuation routes, and provide actionable insights. A flood emergency case study is employed to illustrate the system’s potential to transform urban disaster response.

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Integrating ChatGPT and Geospatial Resources: A Virtual Assistant for Enhanced Decision-Making in Disaster Response

  • Juan Francisco Bustamante-Avila,
  • Luis M. Vilches-Blázquez,
  • Sandra Dinora Orantes-Jiménez

摘要

Natural disasters pose a constant threat to urban populations, causing severe risks to human safety, infrastructure, and the environment. The United States experiences approximately 50 major disasters annually, including hurricanes, wildfires, and floods, often leading to significant challenges in emergency management. Traditional response systems face limitations such as information overload and coordination issues, especially in densely populated areas. To address these challenges, this article explores the integration of Artificial Intelligence (AI) and GIScience to enhance real-time decision-making during crises. This work introduces a conversational virtual assistant based on ChatGPT-4o, designed to process real-time geospatial data and assist citizens during emergencies. The system utilizes Large Language Models (LLMs) and geospatial resources to access, synthesize, and analyze official sources, generate customized evacuation routes, and provide actionable insights. A flood emergency case study is employed to illustrate the system’s potential to transform urban disaster response.