Harnessing Generative AI and LLM for SAR and Emergency Management

Foreword

In this opinion piece, I draw upon my experience in various fields, including Police Search and Rescue, Disaster Victim Identification, Emergency Management, infrastructure resilience, NFP governance, and developing youth development programs. As I explore the subject of Generative AI in Search and Rescue and Emergency Management and its potential impact on humanity’s future, I approach it with a mix of excitement and caution. My work is driven a desire to make a positive impact while recognizing the importance of collaboration and critical thinking.

With a background in locating and rescuing missing individuals, managing mass casualty incidents, and designing emergency response strategies, I bring a diverse perspective to the topic at hand. Additionally, my passion for empowering young minds and utilizing technology to enhance educational experiences has shaped my professional approach. Collaboration with stakeholders and a commitment to professionalism and strong leadership are values I hold dear.

As part of the NZSAR consortium, we have the privilege of working alongside other sector partners to foster innovation and improvement. This opinion piece reflects my thoughts on Generative AI, taking into account both its potential and the ethical considerations it raises.

Steve Campbell | CEO |YSAR Trust

Rationale

This report presents a detailed examination of the potential applications and benefits of generative AI, Large Language Models (LLM), and other AI-integrated technologies in supporting Search and Rescue (SAR) and Emergency Management (EM) resilience, readiness, response, and recovery efforts. By leveraging the capabilities of AI, we can enhance SAR and EM operations by augmenting basic skills and human institutional knowledge, improving response efficiency, and leveraging the strengths of the youth culture as digital natives. The report explores various domains, datasets, and scenarios to identify opportunities for utilizing generative AI and LLM in SAR and EM, including but not limited to Robert Koester’s lost person behavior data sets, police SAR data, medical data, NZSAR Sardonyx reports, GIS data sets, local authorities’ terrain and infrastructure data, and historic and real-time aerial imagery. Additionally, it outlines predictive analysis techniques, the creation of search patterns, and the prioritization of resilience and readiness projects based on historical and present events. The report concludes by calling for further research and collaboration to realize the full potential of AI technologies in SAR and EM.

Introduction

Background and motivation

In recent years, the advancement of generative AI and Large Language Models (LLM) has opened up new possibilities for supporting Search and Rescue (SAR) and Emergency Management (EM) operations. The integration of AI technologies has the potential to enhance the existing capabilities of SAR and EM agencies, leading to improved resilience, readiness, response, and recovery efforts.

Objectives of the report

This report aims to explore the potential applications of generative AI, LLM, and other AI-integrated technologies in SAR and EM. It identifies specific datasets, techniques, and scenarios where AI can be leveraged to augment basic skills and human institutional knowledge, improve response efficiency, and engage the youth culture as digital natives. The report also highlights the need for further research and collaboration to fully unlock the benefits of AI technologies in SAR and EM.

SAR and EM Skills Supported by AI Technology

Integration of generative AI and LLM

The integration of generative AI and Large Language Models (LLM) provides SAR and EM agencies with powerful tools for data analysis, decision support, and information extraction. These technologies can assist in processing large datasets and extracting valuable insights to enhance SAR and EM operations.

Utilizing Robert Koester’s lost person behavior data sets

Robert Koester’s lost person behavior data sets are highly valuable for understanding the behaviors and patterns of missing individuals. Search and rescue (SAR) agencies can leverage generative AI and large language models (LLMs) to analyze these datasets and develop more effective search strategies, thereby increasing the chances of successful rescues. To further enhance the effectiveness of these data sets, it is crucial to build upon Koester’s concepts and develop comprehensive big data sets that incorporate both local and international data feeds. These expanded data sets through systems should encompass a wide range of variables, including demographics, environmental conditions, terrain characteristics, and historical incident data. By incorporating diverse data sources, SAR and emergency management (EM) agencies can improve their understanding of lost person behaviors, refine predictive models, and formulate more accurate search strategies. Collaborations with international partners can play a significant role in enriching the data sets and facilitating cross-context analysis. This collaboration enables SAR and EM agencies to gain valuable insights and shared best practices in search and rescue and emergency management operations. By combining the expertise and data from various regions, agencies can enhance their capabilities and increase the chances of successful outcomes in missing-person cases.

Analyzing Police SAR Records

Historic Police SAR records and case studies contain valuable information about past search and rescue missions. By applying AI technologies to analyze these records, SAR agencies can identify trends, patterns, and lessons learned, allowing for more efficient and targeted response efforts.

Processing medical data

Medical data, including patient records and health information, can be leveraged with AI technologies to enhance emergency medical response. By analyzing medical data in real time, SAR and EM agencies can improve medical triage, resource allocation, and coordination with healthcare providers.

Extracting insights from Sardonyx reports

Sardonyx reports provide detailed information about SAR incidents in New Zealand. By utilizing AI technologies, SAR agencies can extract valuable insights from these reports, identify common challenges, and develop proactive strategies to mitigate risks and improve response outcomes. Sardonyx feeding into LPB datasets has the potential in improving predictive analytics resulting in smarter asset deployment.

Leveraging GIS data sets for spatial analysis

Geographical Information System (GIS) data sets offer valuable spatial information about terrain, infrastructure, and other relevant factors. By integrating generative AI and LLM with GIS data, SAR and EM agencies can improve their understanding of the operational environment, optimize resource allocation, and identify vulnerable areas. GIS offers tools to perform predictive analysis and with LLM and AI integration this technology will be significantly enhanced in the future.

Incorporating local authorities’ terrain and infrastructure data

Collaborating with local authorities to access terrain and infrastructure data enables SAR and EM agencies to better understand the physical characteristics of the area they operate in. AI technologies can help analyze and interpret this data, allowing for more informed decision-making during emergencies.

Enhancing survivability statistics analysis

AI can assist in analyzing survivability statistics and historical data related to SAR and EM incidents. By utilizing generative AI and LLM, agencies can identify factors that contribute to successful outcomes, inform risk assessments, and guide resource allocation strategies.

Integrating historic and real-time aerial imagery for situational awareness

Historic and real-time aerial imagery can provide valuable situational awareness during SAR and EM operations. AI technologies can help process and analyze this imagery, enabling agencies to identify changes in the environment, locate individuals or hazards, and improve overall response effectiveness.

AI-enabled analysis of FLIR ortho mosaic imagery

Forward-Looking Infrared (FLIR) ortho mosaic imagery can provide critical thermal information for locating missing persons or identifying heat signatures during emergencies. AI-enabled analysis of FLIR imagery can automate the process of detecting and analyzing heat patterns, improving response efficiency and accuracy.

Lineal feature recognition for identifying critical areas

AI technologies can assist in lineal feature recognition, which involves identifying critical areas such as road networks, water bodies, and other geographic features. By automatically detecting and analyzing these features, SAR and EM agencies can optimize their response strategies and prioritize resources accordingly.

Weather forecasting using AI

AI will significantly transform weather forecasting by analyzing vast amounts of data, recognizing patterns, and making accurate predictions. Machine learning and predictive modeling enable AI models to learn from historical weather data and generate real-time forecasts. Ensemble forecasting combines multiple models to provide a range of possible outcomes and assess forecast uncertainty. The collaboration between AI and human experts is essential for leveraging AI’s potential in weather forecasting.

Integrating live tracking and real-time field data into AI systems

Integrating live data enhances the decision-making process for Incident Management Teams (IMTs) during responses. By utilizing technologies like GPS, satellite imagery, and IoT sensors, real-time tracking of personnel, vehicles, and resources becomes possible. AI algorithms could process this data to provide IMT decision-makers with an accurate understanding of the operational environment and make informed decisions based on current conditions. This combination of live tracking, real-time data, and AI analysis improves situational awareness, resource efficiency, and overall response outcomes.

Responding Smarter and Faster

Predictive analysis using generative AI

Generative AI can be used to perform predictive analysis, leveraging historical data and patterns to anticipate future SAR and EM scenarios. By applying predictive models, agencies can make informed decisions, allocate resources effectively, and proactively address potential challenges.

Creating search patterns based on historical and environmental factors

AI technologies can assist in creating search patterns based on historical data, environmental factors, and terrain characteristics. By analyzing past incidents and environmental conditions, SAR agencies can generate optimized search patterns to maximize the chances of locating missing individuals or identifying hazards.

Optimizing resource allocation through AI-driven algorithms

AI-driven algorithms can help optimize the allocation of resources during SAR and EM operations. By considering various factors such as incident severity, available resources, and response times, these algorithms can assist agencies in making data-driven decisions to allocate resources effectively and efficiently.

Real-time analysis of aerial imagery and surveillance data

Real-time analysis of aerial imagery and surveillance data can provide valuable insights during SAR and EM operations. AI technologies can be used to process and analyze this data, enabling agencies to detect and respond to emerging situations promptly and effectively.

Integration of IoT sensors for enhanced situational awareness

The integration of Internet of Things (IoT) sensors can enhance situational awareness during SAR and EM operations. AI technologies can process the data collected by these sensors, providing real-time information on environmental conditions, structural integrity, or the presence of hazardous substances.

Incorporated AI-Influenced Real-time RSS and GEORSS

The integration of AI-influenced real-time Really Simple Syndication (RSS) and GeoRSS feeds presents significant opportunities to enhance search and rescue (SAR) and disaster response operations. By leveraging AI technologies, SAR and emergency management (EM) agencies can extract valuable information from diverse data sources and deliver timely, relevant updates to responders and the public.

AI-driven decision support systems for informed decision-making

AI-driven decision support systems can assist SAR and EM agencies in making informed decisions during emergencies. By analyzing multiple data sources, generating recommendations, and considering various scenarios, these systems can provide valuable insights and aid in critical decision-making processes.

Accelerating response times through AI technologies

AI technologies, such as automated alert systems and real-time data analysis, can significantly reduce response times during emergencies. By automating certain tasks, aggregating information, and providing instant notifications, SAR and EM agencies can improve their response speed and overall effectiveness.

Celebrating Youth Culture as Digital Natives

Recognizing the unique perspective and skills of digital natives

The youth culture, often referred to as digital natives, possesses unique perspectives, skills, and familiarity with technology. SAR and EM agencies can leverage this expertise by recognizing and valuing their contributions to developing AI applications and solutions for improved resilience and response.

Engaging youth in the development of AI applications for SAR and EM

Actively involving young volunteers and digital natives in the development of AI applications for SAR and EM can yield innovative and effective solutions. By encouraging collaboration and providing opportunities for young individuals to contribute their ideas and skills, agencies can tap into their creativity and enthusiasm.

Designing user-friendly interfaces for AI-driven decision support systems

User-friendly interfaces play a crucial role in the adoption and effectiveness of AI-driven decision support systems. Agencies should prioritize the design of intuitive interfaces that accommodate the preferences and usability expectations of the youth culture, ensuring seamless interaction and optimal utilization of AI technologies.

Collaboration between young volunteers and developers

Encouraging collaboration between young volunteers and developers fosters an environment of knowledge exchange and innovation. By facilitating partnerships and initiatives that bring together the expertise of digital natives and experienced professionals, SAR and EM agencies can harness the collective power of diverse perspectives.

The Future Horizon of SAR and EM

Utilizing generative AI and LLM for advanced data analysis

The utilization of generative AI and LLM for advanced data analysis holds immense potential for SAR and EM. By leveraging these technologies, agencies can extract deeper insights from vast amounts of data, enabling more accurate predictions, better resource allocation, and improved decision-making.

Expanding predictive analysis capabilities for SAR and EM scenarios

The expansion of predictive analytics capabilities can revolutionize SAR and EM operations. By incorporating additional datasets, refining algorithms, and improving models, agencies can enhance their ability to forecast and prepare for future incidents, minimizing the impact of emergencies.

Integration of social media feeds and public sentiment analysis

Integrating social media feeds and conducting public sentiment analysis can provide valuable real-time information during emergencies. By monitoring social media platforms and analyzing public sentiment, SAR and EM agencies can gain insights into evolving situations, public perception, and potential challenges.

AI-driven chatbots for real-time information and guidance

AI-driven chatbots can be further developed to provide real-time information, guidance, and support to individuals during emergencies. By leveraging natural language processing and advanced algorithms, these chatbots can assist in disseminating critical information, answering inquiries, and offering guidance.

Enhancing coordination between multiple agencies through AI-driven communication platforms

AI-driven communication platforms can improve coordination and collaboration between multiple agencies involved in SAR and EM operations. By facilitating seamless information sharing, real-time updates, and streamlined communication, these platforms can enhance the overall effectiveness and efficiency of response efforts.

Project-Based Learning in the Readiness Phase

In the readiness phase of SAR and EM operations, project-based learning can play a crucial role in preparing responders and stakeholders. Project-based learning involves engaging participants in hands-on projects that simulate real-world scenarios and require problem-solving, critical thinking, and collaboration. By incorporating project-based learning into the readiness phase, SAR and EM agencies can:

Foster practical skills development: Project-based learning allows participants to apply theoretical knowledge to practical situations, enhancing their skills in areas such as data analysis, decision-making, and coordination.
Promote teamwork and collaboration: Projects often require teamwork and collaboration, mirroring the multidisciplinary nature of SAR and EM operations. By engaging participants in collaborative projects, agencies can foster effective teamwork and communication among responders and stakeholders.
Develop adaptability and resilience: Project-based learning exposes participants to varying scenarios and challenges, promoting adaptability and resilience in the face of uncertainty. This prepares them to respond effectively to evolving situations during actual emergencies.
Encourage innovation and creativity: Projects provide opportunities for participants to think innovatively and creatively, exploring new approaches and solutions. This mindset can translate into more effective problem-solving during SAR and EM operations.
Establish a culture of continuous learning: Project-based learning emphasizes continuous learning and improvement. By incorporating this approach in the readiness phase, SAR and EM agencies can foster a culture of ongoing development and adaptation to new challenges and technologies.

Summary

In this opinion piece, I have explored the potential applications and benefits of generative AI, LLM, and other AI-integrated technologies in SAR and EM operations. It has identified specific datasets, techniques, and scenarios where AI can augment basic skills, improve response efficiency, and engage the youth culture. The findings highlight the transformative capabilities of AI in enhancing resilience, readiness, response, and recovery efforts.

To fully realize the potential of AI technologies in SAR and EM, further research and collaboration are recommended. Agencies should focus on refining AI models, expanding data sources, fostering partnerships with youth volunteers and developers, and prioritizing the development of user-friendly interfaces. Continued exploration and investment in AI-driven solutions will contribute to the advancement of SAR and EM practices.

Steve Campbell | YSAR Trust | steve.campbell@ysar.nz