AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting

This paper presents the Open-Source Intelligence Disaster Event Tracker (ODET), a modular platform that provides customizable endpoints and agents for each processing step. ODET enables the implementation of AI-enhanced algorithms to respond to various complex disaster scenarios. To evaluate ODET, we conducted two case studies using unmodified AI models to demonstrate its base performance and potential applications. Through our case studies on Hurricane Harvey and the 2023 Turkey earthquake, we show how complex tasks can be quickly broken down with ODET while achieving a score of up to 89% using the AlignScore metric. ODET enables compliance with Berkeley protocol requirements by ensuring data privacy and using privacy-preserving processing methods. Our results demonstrate that ODET is a robust platform for the long-term monitoring and analysis of dynamic environments and can improve the efficiency and accuracy of situational awareness reports in disaster management.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2024-11-29
DOI
https://doi.org/10.3390/app142311165
Citations
3
Primary Topic
Seismology and Earthquake Studies
Type
article
Field-Weighted Citation Impact
1.04
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting

Klaus Schwarz, Daniel Arias Aranda, Michael Hartmann, Kendrick Bollens
3 citations
Applied Sciences
Seismology and Earthquake Studies
1.04
article

AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting

Klaus Schwarz, Daniel Arias Aranda, Michael Hartmann, Kendrick Bollens
article en
3 citations

Abstract

This paper presents the Open-Source Intelligence Disaster Event Tracker (ODET), a modular platform that provides customizable endpoints and agents for each processing step. ODET enables the implementation of AI-enhanced algorithms to respond to various complex disaster scenarios. To evaluate ODET, we conducted two case studies using unmodified AI models to demonstrate its base performance and potential applications. Through our case studies on Hurricane Harvey and the 2023 Turkey earthquake, we show how complex tasks can be quickly broken down with ODET while achieving a score of up to 89% using the AlignScore metric. ODET enables compliance with Berkeley protocol requirements by ensuring data privacy and using privacy-preserving processing methods. Our results demonstrate that ODET is a robust platform for the long-term monitoring and analysis of dynamic environments and can improve the efficiency and accuracy of situational awareness reports in disaster management.

Applied SciencesVol. 14(23)
Universidad de Granada (ES), SRH University of Applied Sciences Heidelberg (DE)
Climate action
Openalex Percentile: Top 18%
Seismology and Earthquake Studies
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.