Airport Digital Twins for Resilient Disaster Management Response

Airports are constantly facing a variety of hazards and threats from natural disasters to cybersecurity attacks and airport stakeholders are confronted with making operational decisions under irregular conditions. We introduce the concept of the foundational twin, which can serve as a resilient data...

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Bibliographic Details
Published inLearning and Intelligent Optimization Vol. 13621; pp. 467 - 486
Main Author Agapaki, Evangelia
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2023
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Computer vision
Digital twin
Geometric deep learning
Graphical neural network
Instance segmentation
Point clouds
Online AccessGet full text
ISBN9783031248658
3031248651
ISSN0302-9743
1611-3349
DOI10.1007/978-3-031-24866-5_34

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Summary:Airports are constantly facing a variety of hazards and threats from natural disasters to cybersecurity attacks and airport stakeholders are confronted with making operational decisions under irregular conditions. We introduce the concept of the foundational twin, which can serve as a resilient data platform, incorporating multiple data sources and enabling the interaction between an umbrella of twins. We then focus on providing data sources and metrics for each foundational twin, with an emphasis on the environmental airport twin for major US airports.
ISBN:9783031248658
3031248651
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-031-24866-5_34