Big Data Analytics for Natural Disaster Management (NDM)

December 13, 2023


Ioannis Pitas (AUTH),

Nikolaos Militsis (AUTH),

Christos Papaioannidis (AUTH),

Jose Ramiro Martinez De Dios (U Seville),

Panagiotis Katsaros (AUTH),

Giovanni Giacco (Latitudo40),

Antonio Filograna (Engineering),

Resch Bernd (PLUS),

Massimo Villari (UNIME),

Jacques Coves (CS GROUP),

Evangelos Spatharis (AUTH),

Content and organization

This short course on Big Data Analytics for Natural Disaster Management (NDM) provides a comprehensive overview and in-depth presentation of advanced technologies involved in the acquisition and analysis of Big Data for NDM. NDM can be greatly improved by developing automated means for precise semantic mapping and phenomenon evolution predictions in real-time. Several extreme data sources can significantly help towards achieving this goal: a) autonomous devices and smart sensors at the edge, equipped with AI-capabilities; b) satellite images; c) topographical data; d) official meteorological data, predictions or warnings published in the Web; and e) geosocial media data (including text, image and video). Such heterogeneous data sources provide a prime extreme data example: a) they are diverse; b) voluminous; c) fast and frequently updated; d) complex; e) multilingual; f) have very disperse sources (satellite, drone, sensors, social media, maps); and h) have extreme values.

The course consists of ten lectures, covering important topics and presenting state-of-the-art technologies in: a) Big Data acquisition using sensors, drones, satellites, the Web and social media platforms; b) Big Data analysis based on Deep Learning; c) accurate phenomena modeling; and d) analysis, forecasting and risk management for improved NDM. The presented technologies find practical application in developing an advanced NDM support system that dynamically exploits multiple data sources and AI technologies for providing an accurate assessment of an evolving crisis situation.

Hybrid event

All lectures and workshops will be delivered on-site in KEDEA buidling or remote via Zoom (Passcode: 405011).

This short course is supported by Horizon Europe TEMA project.

Course Duration

8 hours and 55 minutes (10 lectures)

Course Type

Short Course

Participation terms

Both AIDA and non AIDA students, CS/ECE/EE/AI students/scientists, engineers as well as AI enthusiasts from other scientific disciplines having the necessary mathematical background are welcomed to register free of charge on a First-Come-First-Serve basis. Please find the Registration Form.
If you are an AIDA Student* already, on top of the above registration, enroll on this course using the button "ENROLL ON THIS COURSE" below, so that this course is included on your AIDA Certificate of Course Attendance, upon successful course participation (attendance of at least 80% of the scheduled course lectures).
*AIDA Students should have been registered in the AIDA system already (they are PhD students or PostDocs that belong only to the AIDA Members list.



Modality (online/in person):

Both in person and online

Host Institution
Aristotle University of Thessaloniki

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