This study presents an innovative, integrated system for autonomous event detection and delineation of areas of interest in natural disaster scenarios. To achieve this goal, the system leverages a variety of data sources, such as social media and news aggregators, and analyzes the collected data to detect relevant events and precisely define the boundaries of the affected areas. The system ha been implemented and extensively tested across a variety of events, from floods to droughts and earthquakes, with the intent of highlighting its remarkable ability to provide timely detection and an accurate estimate of the area of interest.
Questo studio presenta un innovativo sistema integrato che mira a rilevare autonomamente disastri naturali e a identificare le aree colpite. Per raggiungere questo obiettivo il sistema sfrutta diverse fonti come social media e aggregatori di notizie, analizzando i dati raccolti per rilevare eventi di interesse e definire con precisione i confini delle aree interessate. Il sistema è stato implementato e sottoposto a un’ampia varietà di test su eventi che spaziano dalle alluvioni a siccità e terremoti, con l’obiettivo di evidenziare la sua notevole capacità di rilevare tempestivamente gli eventi e fornire una stima accurata delle aree colpite.
Automated event detection and delineation of the area of interest for natural emergencies
Terraneo, Leonardo
2023/2024
Abstract
This study presents an innovative, integrated system for autonomous event detection and delineation of areas of interest in natural disaster scenarios. To achieve this goal, the system leverages a variety of data sources, such as social media and news aggregators, and analyzes the collected data to detect relevant events and precisely define the boundaries of the affected areas. The system ha been implemented and extensively tested across a variety of events, from floods to droughts and earthquakes, with the intent of highlighting its remarkable ability to provide timely detection and an accurate estimate of the area of interest.File | Dimensione | Formato | |
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Thesis.pdf
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Executive_Summary.pdf
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3.34 MB | Adobe PDF | Visualizza/Apri |
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https://hdl.handle.net/10589/222453