Abstract

Building on the work initially conducted during the SURP 2021 project and extended through 2021–2023, this initiative focuses on leveraging computer technology to enhance the search-and-rescue (S&R) process for locating missing persons. Traditionally, paper-based S&R forms are manually completed, collected, and reviewed, introducing inefficiencies and potential errors. This project digitized approximately 30 S&R forms, including the complex Lost Person Questionnaire, and developed infrastructure to collect, process, and analyze this data.

By integrating Artificial Intelligence and Machine Learning, this project aims to combine ongoing search efforts, historical mission data with similar profiles, and general knowledge like terrain and travel routes. The goal is to identify high-priority areas for S&R missions, enhancing the effectiveness of search coordinators and reducing reliance on manual processes.

Tools & Technologies

• Semantic Tools: PoolParty Semantic Suite, Protégé
• Data Integration: Digitized S&R forms and centralized data processing infrastructure
• AI/ML Techniques: Pattern recognition, profile matching, terrain modeling

Impact

The ISRID3 Ontology project enhances search-and-rescue efforts by addressing inefficiencies in the manual processing of S&R data. Through AI/ML integration, this project:

• Reduced time required for data collection and analysis
• Improved accuracy and prioritization of search areas
• Minimized human error and loss of critical information
• Enabled scalable integration with historical and ongoing mission data

Learn More

Explore more about this project in the California Polytechnic State University SURP Repository.