Coactive Design of Human-machine Collaborative Damage Assessment Using UAV Images and Decision Trees

Wang, Zhichao; Wu, Lizhen; Wang, Chang; Wen, Xupeng; Niu, Yifeng; Yin, Dong

doi:10.1109/cac.2018.8623443

Cited by 5 publications

(1 citation statement)

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“…The authors developed three dimensional (3D) models based on these collected data using the structure-from-motion technique to evaluate damage situation. In [46], UAV images of a site before and after destruction are used to estimate the amount of damage. UAV-based mapping is utilized for quick assessment of the damage caused by volcano eruption through change detection analysis in [47].…”

Section: Assessmentmentioning

confidence: 99%

Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management

et al. 2019

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Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field.

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Section: Assessmentmentioning

confidence: 99%