Deep learning and stereo vision based detection of post-earthquake fire geolocation for smart cities within the scope of disaster management: İstanbul case

Kustu, Tacettin; Gümüş, Alev Taşkın

doi:10.1016/j.ijdrr.2023.103906

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…One of the most destructive and frequent secondary disasters is post-earthquake fires. Early fire detection systems exist to minimize losses caused by fires in the potentially chaotic environment that can occur after earthquakes in cities (Kustu & Taskin, 2023). This system consists of a structure that detects fires with a Convolutional Neural Network (CNN), useful for post-earthquake fire detection with low cost, high reliability, and accuracy.…”

Section: Security and Resiliencementioning

confidence: 99%

Neurotechnologies Applied to Society's Perception of Cyber-Physical Systems (CPS) in Smart Cities

Juárez-Varón,

Gaón,

Mengual-Recuerda

et al. 2024

International Journal on Semantic Web and Information Systems

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This study aims to quantify the perception of value and acceptance by citizens of the use of cyber-physical systems (CPS) in transportation systems and smart cities using neurotechnologies. The work has been developed in the main cities of the following Latin American countries: Spain, Ecuador, Colombia, and Argentina. Targeting urban, public transport-using graduates, it assesses CPS in smart cities and user experiences. Triangulating qualitative research and neurotechnology, the study extends the taxonomy of emotional domains. The results indicate that users do not always assign equivalent importance to what they truly feel, and it is noteworthy that the most important factor, both quantitatively and emotionally, is the application of CPS to improve efficiency in public transportation. The implications of these analyses are discussed in the final part of the article with the aim of providing recommendations to policymakers on the key aspects to be considered in the design and development of CPS for use in smart cities.

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Section: Security and Resiliencementioning

confidence: 99%