AI-Assisted Disaster Response

Project Summary:

Natural disasters, particularly large-scale events such as major earthquakes and extreme weather events, can overwhelm emergency response capabilities. These events often cause widespread damage and disrupt communication infrastructure, making it challenging for first responders to navigate and coordinate their efforts effectively. To address these challenges, we propose the development of an AI-assisted disaster response system that leverages large language models, temporal-spatial machine learning, and interactive dashboards to enhance situational awareness and optimize response operations.

Project Objectives:

1.    Accelerate Onboarding of First Responders: Develop an AI-powered knowledge base that provides real-time access to critical information, including maps, hazard reports, and emergency protocols, to facilitate rapid onboarding of first responders unfamiliar with the disaster area.

2.    Reduce Cognitive Load of Dispatchers: Implement a machine learning system that analyzes real-time data feeds, including sensor data, social media, and emergency calls, to identify patterns, predict potential hazards, and prioritize dispatch decisions, reducing the cognitive burden on dispatchers.

3.    Navigation and Geocoding System for Demolished Urban Areas: Develop a navigation system that utilizes real-time satellite imagery, machine learning algorithms, and crowd-sourced data to create dynamic maps and routing solutions for demolished urban areas, enabling first responders to navigate effectively in challenging environments.

4.    Damage Assessment System Based on Human Mobility: Design a machine learning model that analyzes anonymized human mobility data, such as mobile phone location data, to infer damage severity and identify areas requiring immediate attention, facilitating efficient resource allocation and damage assessment.

Related Work:

Challenge 1: Accelerate Onboarding of First Responders

1.    "An Intelligent Knowledge Base for Emergency Management Using Natural Language Processing" by Wu et al., (2018)

This paper proposes an intelligent knowledge base system that utilizes natural language processing techniques to extract and organize information from various sources, providing first responders with a comprehensive overview of the disaster situation.

Challenge 2: Reduce Cognitive Load of Dispatchers

2.    "A Machine Learning Approach to Emergency Dispatch Optimization" by Johnson et al., (2019)

This paper describes a machine learning approach that utilizes historical data and real-time incident information to predict call volume and dispatch resource requirements, optimizing dispatcher workloads and improving response times.

Challenge 3: Navigation and Geocoding System for Demolished Urban Areas

3.    "A Dynamic Routing System for Emergency Response in Disrupted Urban Environments" by Chen et al., (2020)

This paper proposes a dynamic routing system that integrates real-time traffic data, satellite imagery, and machine learning algorithms to generate optimal routes for first responders in dynamically changing urban environments.

Challenge 4: Damage Assessment System Based on Human Mobility

4.    "Inferring Urban Damage from Human Mobility Data" by Kang et al., (2021)

This paper demonstrates the use of machine learning algorithms to analyze anonymized human mobility data to infer the extent of damage in disaster-affected areas, providing valuable insights for resource allocation and damage assessment.

Possible Partners/Roles:

1.    Domain Expert – First Responder: Provide expertise on disaster response protocols, first responder needs, and the challenges of operating in unfamiliar environments.

2.    Domain Expert – Urban Planner: Contribute knowledge of urban infrastructure, layout, and potential hazards, facilitating the development of effective navigation and damage assessment tools.

3.    Technology – Disaster Response Planning Software: Collaborate with developers of disaster response planning software to integrate AI capabilities and enhance decision-making support.

4.    Technology – Dispatcher Software: Integrate AI-powered dispatch optimization algorithms into existing dispatcher software to streamline dispatch operations and reduce cognitive load.

5.    Research – Visual Analytics: Collaborate with visual analytics researchers to develop interactive dashboards that effectively communicate real-time data and insights to decision-makers.

6.    Research – Human Computer Interaction: Ensure the usability and effectiveness of AI-powered tools, considering the needs and expectations of first responders and disaster management personnel.

7.    Research – Spatio Temporal Data Processing: Develop innovative algorithms for processing and analyzing spatiotemporal data, such as satellite imagery and human mobility data, to extract actionable insights for disaster response.