Research On Incentive Mechanism Of Remote Sensing Information Crowdsourcing And High Elasticity Technology Of Its Supporting Platform

With the rapid development of information technology and the popularity of the Internet,personal computers and mobile devices,people have entered the era of Web 2.0.People have more opportunities to contribute knowledge or other forms of labor.Crowdsourcing model is the product of Web 2.0 environment.It is an outsourcing model that the outsourcer assigns tasks to the public in order to obtain goods or services.Crowdsourcing of remote sensing information is a crowdsourcing model that encourages the public to participate in remote sensing information related projects and submit spatial information of actual objects.It plays an important role in meteorological information acquisition,disaster emergency response,land use,urban planning,environmental monitoring and species protection.Scientists have carried out extensive research on crowdsourcing model of remote sensing information from the aspects of participation motivation,quality of contribution data and technologies on supporting platform,and have achieved some results.However,the poor quality of data submitted by the public and the low participation in the project are the difficulties in the implementation of remote sensing information crowdsourcing projects.In addition,some hot events can easily cause access storms and lead to the collapse of supporting platforms.The purpose of this paper is to solve these problems.Only be subjected to material or psychological incentives,can people have a stronger motivation to participate in the spatial crowdsourcing project.Because of people's intelligence and rationality,we cannot rule out the possibility of speculation and fraud by malicious participants,so we must design a reasonable incentive mechanism.In this paper,we introduce the utility theory and define remote sensing information crowdsourcing as a contract-free transaction between the contractor and the participants.From the perspective of game theory,we design incentive mechanism to improve the psychological motivation of public participation and submission of highquality information.In addition,we studied the application of document flow model and container cloud technology in the support platform of remote sensing information crowdsourcing,and built a public data product aggregation system and a remote sensing information crowdsourcing visual interpretation system which can cope with the access storm and be applied to conventional spatial data collection and disaster emergency response.The main contributions and innovations are as follows:By introducing von Neumann-Morgenstern's utility theory and game theory,we designed an incentive mechanism based on Bayesian game.The mechanism relaxes the restriction of Gibbard-Satterthwaite's impossibility theorem into Bayesian game,which makes the beliefs of the outsourcer and the participants consistent with each other.The two sides can only achieve a very weakly dominant strategy equilibrium,so there is no dictatorial strategy between them.Under this mechanism,the expected utility of participants submitting high-quality information is higher than that of poor-quality information,and the risk function shows that the best strategy of participants is to submit the correct spatial information of the target objects in all the contracting areas.Participants have no motivation to speculate and submit poor-quality information,so the mechanism is incentive compatible.It can greatly improve the data quality of remote sensing information crowdsourcing while attracting a large number of participants.We propose a geometric primitive comparison algorithm based on Jaccard coefficients.By setting buffers based on the boundary of geometric primitives,Jaccard coefficients are calculated based on the buffers of two primitives to judge the coincidence degree.The algorithm can automatically calculate participants' utility and integrate data.Based on the document flow model,we designed and implemented a spatial information database,which unifies the operation of adding,deleting and modifying data into adding operation,reduce the complexity of spatial information storage to one order,and greatly alleviate the pressure on the serve.We built a highly elastic deployment environment based on container cloud technology to achieve automatic scaling of computing resources.With Bayesian game-based incentive mechanism and document flow-based database,we built a public data product aggregation system and a remote sensing image crowdsourcing visual interpretation system.These two systems have high elasticity,which can save computing resources,deal with access storms,and bring the public power into full play in conventional spatial data collection and disaster emergency response.