Crowdsourcing, data and information fusion and situation awareness for emergency Management of forest fires: The project DF100Fogo (FDWithoutFire)

2019 ◽  
Vol 77 ◽  
pp. 101172 ◽  
Author(s):  
Allan C.M. Oliveira ◽  
Leonardo C. Botega ◽  
Jordan F. Saran ◽  
Jordana N. Silva ◽  
Jéssica O.S.F. Melo ◽  
...  
2009 ◽  
Vol 10 (1) ◽  
pp. 2-5 ◽  
Author(s):  
Mieczyslaw M. Kokar ◽  
Gee Wah Ng

Author(s):  
Akhila Manne ◽  
Madhu Bala Myneni

Social media has redefined crisis management in the recent years. Extraction of situation awareness information from social media sites such as Twitter, Facebook, Instagram, etc. is a non-trivial task once the required framework is established. Unfortunately, most public safety authorities are still suspicious of using social media in engaging and disseminating information. This chapter reports on how social media can be effectively used in the field of emergency management along with the opportunities and challenges put forth. The chapter starts with a discussion on the functions of social media and its trustworthiness. It provides a description of the framework for disaster management system and the methodology to be adopted. The methodology consists of volunteer classification, methods of data collection, challenges faced, event detection, and data characterization with currently available disaster management tools. The chapter concludes with the division between practice and research and moves toward envisioning how social media may be used as a resource in emergency management.


Author(s):  
Leonardo Castro Botega ◽  
Allan Cesar Moreira de Oliveira ◽  
Valdir Amancio Pereira Junior ◽  
Jordan Ferreira Saran ◽  
Lucas Zanco Ladeira ◽  
...  

Author(s):  
Syed Nasir Danial ◽  
Jennifer Smith ◽  
Faisal Khan ◽  
Brian Veitch

Abstract Situation awareness is the first and most important step in emergency management. It is a dynamic step involving evolving conditions and environments. It is an area of active research. This study presents a Markov Logic Network to model SA focusing on fire accidents and emergency evacuation. The model has been trained using empirical data obtained from case studies. The case studies involved human participants who were trained for responding to emergencies involving fire and smoke using a virtual environment. The simulated (queried) and empirical findings are reasonably consistent. The proposed model enables implementing an agent that exploits environmental cues and cognitive states to determine the type of emergency currently being faced. Considering each emergency type as a situation, the model can be used to develop a repertoire of situations for agents so that the repertoire can act as an agent’s experience for later decision-making.


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