Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images

Dou, Maowei; Chen, Jingying; Chen, Dan; Chen, Xiaodao; Deng, Ze; Zhang, Xuguang; Xu, Kai; Wang, Jian

doi:10.1016/j.future.2013.12.018

Cited by 35 publications

(14 citation statements)

References 34 publications

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“…In this study, the authors proposed an augmented reality simulation environment for testing the effectiveness of IT solutions for disaster response using multi-agent simulation (modeling each person involved as an agent). In recent years, Dou et al [38] proposed an agent-based framework for human rescue operations in landslide disaster events to evaluate the contingency plan. The proposed framework uses high-resolution remote sensing images, simulates a landslide environment based on a three-dimensional landslide geological model, and uses a multi-agent simulation approach to provide individuals' behavior simulation under dynamic disaster scenarios.…”

Section: Simulationmentioning

confidence: 99%

Review of Big Data and Processing Frameworks for Disaster Response Applications

Cumbane

Gidófalvi

2019

IJGI

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Natural hazards result in devastating losses in human life, environmental assets and personal, and regional and national economies. The availability of different big data such as satellite imageries, Global Positioning System (GPS) traces, mobile Call Detail Records (CDRs), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning) can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response systems development usually requires the integration of data from different sources (streaming data sources and data sources at rest) with different characteristics and types, which consequently have different processing needs. Deciding which processing framework to use for a specific big data to perform a given task is usually a challenge for researchers from the disaster management field. Therefore, this paper contributes in four aspects. Firstly, potential big data sources are described and characterized. Secondly, the big data processing frameworks are characterized and grouped based on the sources of data they handle. Then, a short description of each big data processing framework is provided and a comparison of processing frameworks in each group is carried out considering the main aspects such as computing cluster architecture, data flow, data processing model, fault-tolerance, scalability, latency, back-pressure mechanism, programming languages, and support for machine learning libraries, which are related to specific processing needs. Finally, a link between big data and processing frameworks is established, based on the processing provisioning for essential tasks in the response phase of disaster management.

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Section: Simulationmentioning

confidence: 99%

Review of Big Data and Processing Frameworks for Disaster Response Applications

Cumbane

Gidófalvi

2019

IJGI

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“…In the normal time, for maintains of the data collection, sensor management and other processing, it needs some running instances in the evacuation service. To simplify the problem, we use simple model [15] to describe the environment for sensor located. Therefore, , we use S m to denote request for maintain the normal service and the minimum service capacity should satisfy (6).…”

Section: The Idme Problemmentioning

confidence: 99%

Multicloud-Based Evacuation Services for Emergency Management

Dong

Ota

et al. 2014

IEEE Cloud Comput.

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A smart evacuation needs a scalable and flexible system to provide service in both emergency and normal situation. A single cloud service is usually limited to support scaling up requirement in emergency especially with a large geographic scope. In this article, we propose MCES, a multi-cloud architecture that deploy the smart evacuation services in multiple cloud providers, which can tolerant larger pressure than single cloud-based services. Usually, this system maintains basic service to support monitoring, while in emergency, the visits to the service will scale up enormously. This means MDSE supports fast scaling up service capacity in a short time. We use a three layer cloud instance management to support rapid capacity scaling up in MCES. By conducting extensive simulations, we demonstrate that our proposed MCES significantly outperforms single cloud solutions under various emergency settings. Index Terms-Evacuation Services; Emergency Management; Multiple Cloud. I. RELATED WORKS A. Cloud-based Evacuation Services There some existing works propose prototypes that adopt cloud computing in the evacuation system. Chu et al. [1] present a hybrid building fire evacuation system with mobile phones and cloud computing. In their prototype, they put the rout computing into the cloud service and use mobile phone to get the sensor information. However, they only move the evacuation services from local to the cloud simply without any consideration about the emergency management. Chen et al. [2] present a system about rescue service with mobile cloud computing. From their work, the rescue system is similar with evacuation system which needs a rescue service to find the evacuation route and also need the information from

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“…Cloud computing has been identified as a potential solution to address some of the big data challenges in remote sensing [7,8,9,10] and big data computing [11,12,13,14], such as allowing massive remote sensing data storage and complex data processing, providing on-demand services [15,16,17], and improving the timeliness of remote sensing information service delivery. For example, Lv, Hu, Zhong, Wu, Li and Zhao [18] demonstrated the feasibility of using MapReduce and parallel K-means clustering for remote sensing image storage and processing.…”

Section: Introductionmentioning

confidence: 99%

A cloud-based remote sensing data production system

Yan

Wang

et al. 2018

Future Generation Computer Systems

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The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts "master-slave" architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a "minimum data transferring" strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, $ Fully documented templates are available in the elsarticle package on CTAN.

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Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images

Cited by 35 publications

References 34 publications

Review of Big Data and Processing Frameworks for Disaster Response Applications

Review of Big Data and Processing Frameworks for Disaster Response Applications

Multicloud-Based Evacuation Services for Emergency Management

A cloud-based remote sensing data production system

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