dc.contributor.author | Schwarz, Klaus | |
dc.contributor.author | Bollens, Kendrick | |
dc.contributor.author | Arias Aranda, Daniel | |
dc.contributor.author | Hartmann, Michael | |
dc.date.accessioned | 2024-12-03T09:22:51Z | |
dc.date.available | 2024-12-03T09:22:51Z | |
dc.date.issued | 2024-11-29 | |
dc.identifier.citation | Schwarz, K. et. al. Appl. Sci. 2024, 14, 11165. [https://doi.org/10.3390/app142311165] | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/97636 | |
dc.description.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. | es_ES |
dc.description.sponsorship | Universidad de Granada and the European
Union’s Horizon 2020 research and innovation programme under grant agreement No [823759]
called REMESH | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | open-source intelligence (OSINT) | es_ES |
dc.subject | artificial intelligence | es_ES |
dc.subject | disaster management | es_ES |
dc.title | AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting | es_ES |
dc.type | journal article | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/823759 | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3390/app142311165 | |
dc.type.hasVersion | VoR | es_ES |