Xicheng Tan

Cloud- and Agent-Based Geospatial Service Chain: A Case Study of Submerged Crops Analysis During Flooding of the Yangtze River Basin

More intelligent construction of geospatial service chains and more efficient execution of such service chains remain major challenges in distributed geospatial analysis. This study addresses these challenges using a Cloud- and agent-based approach for automatic and intelligent construction of a geospatial service chain in the Cloud environment. A spatial agent infrastructure comprising fundamental services and an agent interface is designed, implemented, and deployed. Our approach involves a strategy for selecting and aggregating appropriate agents and Web-processing services (WPS) by evaluating their availability. This strategy ensures successful construction of a geospatial service chain in the Cloud environment, even when there is a lack of requisite geospatial services in the system. Moreover, the method can significantly increase the speed of a service chain in distributed environments and retains high stability when more requests are submitted over various network conditions. This is because the computing mobility and intelligence of the agent help to avoid transfer of large volumes of spatial data and keep the load balanced during construction and execution of the service chain. A prototype system for analysis of submerged crops during flooding of the Yangtze River basin demonstrates the advantages of our approach over existing methods.

Agent-as-a-service-based geospatial service aggregation in the cloud

An Agent-as-a-Service (AaaS)-based geospatial service aggregation is proposed to build a more efficient, robust and intelligent geospatial service system in the Cloud for flood emergency response. It involves an AaaS infrastructure, encompassing the mechanisms and algorithms for geospatial Web Processing Service (WPS) generation, geoprocessing and aggregation. The method has the following advantages: 1) it allows separately hosted services and data to work together, avoiding transfers of large volumes of spatial data over the network; 2) it enriches geospatial service resources in the distributed environment by utilizing the agent cloning, migration and service regeneration capabilities of the AaaS, solving issues associated with lack of geospatial services to a certain extent; 3) it enables the migration of services to target nodes to finish a task, strengthening decentralization and enhancing the robustness of geospatial service aggregation; and 4) it helps domain experts and authorities solve interdisciplinary emergency issues using various Agent-generated geospatial services. Display Omitted Agent-as-a-Service (AaaS)-based geospatial service aggregation on the Cloud is proposed.It allows separately-hosted services and data to work together, which avoids transferring large volume of spatial data.It enriches geospatial service resources in the distributed environment and solves the issue of lack of geospatial services.It strengthens decentralization and enhances robustness of the geospatial service aggregation.It provides experts assistance in solving the interdisciplinary emergency issues with agent-generated geospatial services.

CyberConnector: a service-oriented system for automatically tailoring multisource Earth observation data to feed Earth science models

Feeding multisource Earth observation (EO) data into Earth science models (ESM) remains a daunting challenge. This paper presents a service-oriented approach as an alternative solution. It uses geospatial web services to process the EO data and geoprocessing workflow for automation. Different from existing approaches, it takes advantage of virtual data products (VDP) to release modelers from intensive data processing. It can directly connect ESMs to public EO sources via Cyberinfrastructure. A prototype called CyberConnector is implemented. CyberConnector supports intuitive building of VDP, automatic execution of workflows and effortless retrieval of model-ready input files. We used it to stream multiple datasets to several ESMs including finite-volume coastal ocean model (FVCOM) and cloud-resolving model (CRM). The results show that CyberConnector can truly benefit modelers on time saving and effort minimizing.

GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval

Abstract It is still a great challenge to efficiently deliver dynamic and heterogeneous Earth observation (EO) information to users based on their real time locations. However, the rapidly evolving techniques create a chance to meet the challenge. This paper proposes a framework to realize a one-stop and location based service (LBS) for geospatial information (GI) retrieval on mobile platforms. The framework originally integrates a number of state-of-the-art techniques with geospatial data resources and let them cooperate together to provide a robust and highly available LBS. Cloud platform is used to deploy the server module. A location enabled load balancing algorithm is presented to balance the cloud instance VMs on behalf of LBS. A system named GeoFairy is implemented. It provides a one-stop service for gathering and delivering twelve kinds of GI on real time locations. Two Apps are built for the major mobile ecosystems: iOS and Android. Many tests, including a stress test, have been made via a number of mobile devices at various locations. The results demonstrate that GeoFairy is capable of one-stop delivering real-time GI to users and significantly reducing costs on information searching and retrieving. This feature is very helpful in many scenarios such as disaster responding and military actions. This research paves a way on both theoretical and practical aspects for researchers and developers to realize operational mobile applications for one stop and location based GI retrieval.

Virtual Reality in Smart City

Based on the current cutting-edge technologies, smart city becomes more and more popular in China. Briefly, smart city can provide various services for every citizens and outlanders in a lot of fields, such as tourism, transportation, management and so on. Since smart city contains so many contents, we just focus on one side of it —— visualization. Through the help of virtual reality, the visualization can reach a new level compared to the historical systems, which just utilized common visual display devices; in the new systems, not only eyes, but also ears, hands and even nose can feel the existence of the whole city. In this paper, we’d like to put forward some opinions on the combination of virtual reality and smart city. A detail design of virtual reality framework is put forward in third section. In section four, an actual project follows the blueprint is raised up for practical discussion.

Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud

To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS method includes both parallel architecture and a mechanism for adjusting the computational resources—the parallel geocomputing mechanism of the P-AaaS method used to execute a geospatial service and the execution algorithm of the P-AaaS based geospatial service chain, respectively. The P-AaaS based method has the following merits: (1) it inherits the advantages of the AaaS-based method (i.e., avoiding transfer of large volumes of remote sensing data or raster terrain data, agent migration, and intelligent conversion into services to improve domain expert collaboration); (2) it optimizes the low performance and the concurrent geoprocessing capability of the AaaS-based method, which is critical for special applications (e.g., highly concurrent applications and emergency response applications); and (3) it adjusts the computing resources dynamically according to the number and the performance requirements of concurrent requests, which allows the geospatial service chain to support a large number of concurrent requests by scaling up the cloud-based clusters in use and optimizes computing resources and costs by reducing the number of virtual machines (VMs) when the number of requests decreases.

Developing a web-based system for supervised classification of remote sensing images

Web-based image classification systems aim to provide users with an easy access to image classification function. The existing work mainly focuses on web-based unsupervised classification systems. This paper proposes a web-based supervised classification system framework which includes three modules: client, servlet and service. It comprehensively describes how to combine the procedures of supervised classification into the development of a web system. A series of methods are presented to realize the modules respectively. A prototype system of the framework is also implemented and a number of remote sensing (RS) images are tested on it. Experiment results show that the prototype is capable of accomplishing supervised classification of RS images on the Web. If appropriate algorithms and parameter values are used, the results of the web-based solution could be as accurate as the results of traditional desktop-based systems. This paper lays the foundation on both theoretical and practical aspects for the future development of operational web-based supervised classification systems.