A Framework for Sharing and Integrating Remote Sensing and GIS Models Based on Web Service

Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin

doi:10.1155/2014/354919

Cited by 14 publications

(6 citation statements)

References 47 publications

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“…The SOS [34,35,36] offers pull-based access to sensor measurements or metadata and provides standardized access to sensor observations and sensor metadata [37,38]. Therefore, SOS provides the capability of accessing spatio-temporal trajectory data.…”

Section: Impala-enabled Spatio-temporal Mass Data Sharing Methodologymentioning

confidence: 99%

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City

Zhou

Chen

Yuan

et al. 2016

Sensors

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The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems.

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Section: Impala-enabled Spatio-temporal Mass Data Sharing Methodologymentioning

confidence: 99%

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City

Zhou

Chen

Yuan

et al. 2016

Sensors

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“…The mainstream First, the 'black box' theory is introduced for execution body encapsulation. A black box is a method that can be used without the users knowing how its inner algorithm works [39,40], and the users only need to know the input and output characteristics. Using this approach, the executable code written in any programming language can be encapsulated into a corresponding component object model (COM), such as a dynamic link library (DLL) or an executable program (EXE) that exposes only the interface of the input and output data and hides all of the implementation details.…”

Section: Constraint Rule-based Automatic Service Compositionmentioning

confidence: 99%

A Web Service-Oriented Geoprocessing System for Supporting Intelligent Land Cover Change Detection

Xing

Chen

et al. 2019

IJGI

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Remotely sensed imagery-based change detection is an effective approach for identifying land cover change information. A large number of change detection algorithms have been developed that satisfy different requirements. However, most change detection algorithms have been developed using desktop-based software in offline environments; thus, it is increasingly difficult for common end-users, who have limited remote sensing experience and geographic information system (GIS) skills, to perform appropriate change detection tasks. To address this challenge, this paper proposes an online geoprocessing system for supporting intelligent land cover change detection (OGS-LCCD). This system leverages web service encapsulation technology and an automatic service composition approach to dynamically generate a change detection service chain. First, a service encapsulation strategy is proposed with an execution body encapsulation and service semantics description. Then, a constraint rule-based service composition method is proposed to chain several web services into a flexible change detection workflow. Finally, the design and implementation of the OGS-LCCD are elaborated. A step-by-step walk-through example for a web-based change detection task is presented using this system. The experimental results demonstrate the effectiveness and applicability of the prototype system.

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“…In the same way as in data sharing research, researchers first attempted to study the sharing of models by defining model service metadata [32]. A "black box" was employed to publish heterogeneous remote sensing and GIS models to standard Web services for sharing and interoperation and then to integrate the remote sensing and GIS models using Web services, based on the geospatial workflow and semantic supported marching method [33]. Due to the complexity and disciplinary aspects of remote sensing models, various participants are needed to engage in the collaborative modelling process: model resources are provided by model providers, computing resources by computing resource providers, data resources by data resource providers, and the services of the published model are accessed by users.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hierarchical Representation Approach of Remote Sensing Application Modeling Based on Distributed Environment

Zou

2020

Mathematical Problems in Engineering

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In Earth science, information science, space science, and other disciplines, scientists use the land surface parameter inversion method in their work, applying this to the atmosphere, vegetation, soil, drought, and so on. Multidisciplinary experts sometimes collaborate on a particular application. However, these remote sensing models do not have a unified method of description and management and cannot effectively achieve the sharing of models and data resources. It is also hard to meet user demand for global data and models in the current state, especially in the face of global problems and long-term series problems. In this paper, we examine the scientific questions of the computability and scalability of remote sensing models. This paper adopts a data dependency approach to describe a remote sensing model and implements a hierarchical unified description and management method using modelling based on four layers: a data-processing view, an atomic model view, an on-demand resource package view, and a workflow view. We choose three typical remote sensing models for disaster monitoring as use cases and describe the practical application process of the proposed method. The results demonstrate the advantages and powerful capabilities of this efficient method.

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A Framework for Sharing and Integrating Remote Sensing and GIS Models Based on Web Service

Cited by 14 publications

References 47 publications

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City

A Web Service-Oriented Geoprocessing System for Supporting Intelligent Land Cover Change Detection

An Efficient Hierarchical Representation Approach of Remote Sensing Application Modeling Based on Distributed Environment

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