DESCRIPTION
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). The course focus will be on drone image analysis for Natural Disaster Management.
The course consists of 4 lectures, covering important topics and presenting state-of-the-art technologies in:
- Sensors and Big Visual Data Analytics for Natural Disaster Management (NDM)
- Wildfire Image Analysis
- Flood Image Analysis
- Flood and Wildfire Simulation
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.
LECTURE LIST
- Sensors and Big Visual Data Analytics for Natural Disaster Management (NDM)
- Wildfire Image Analysis
- Flood Image Analysis
- Flood and Wildfire Simulation
This short course overviews research topics dealt in European R&D project TEMA.
Prof. Ioannis Pitas is the coordinator of this big R&D project (20 University and company partners).
CVML WEB LECTURE MODULE SCHEDULE
This module has been designed to be mastered within 1 month (or less), if you have proper background (at least early undergraduate student in an EE, ECE, CS, CSE or any Engineering or Exact Sciences Department).
We propose that you follow the above mentioned Lecture order. You may want to skip few Lectures that might not be of immediate interest to you for later study.
On average you can study 4 lectures per week. The related effort is as follows:
Lecture pdf study and filling the related understanding questionnaire: 1-2 hours per lecture (on average, depending on your background).
LECTURER SHORT CV
Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and Ph.D.Degree in Electrical Engineering, both from the Aristotle University of Thessaloniki, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University. He served as a Visiting Professor at several Universities. His current interests are in the areas of image/video processing, machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He is also chair of the Autonomous Systems initiative.