Distributed Geoscience Algorithm Integration Based on OWS Specifications: A Case Study of the Extraction of a River Network

• Published in: ISPRS international journal of geo-information Vol. 8; no. 1; p. 12
• Main Authors: Tan, Xicheng, Di, Liping, Zhong, Yanfei, Chen, Nengcheng, Huang, Fang, Wang, Jinchuan, Sun, Ziheng, Khan, Yahya Ali
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2019
• Subjects: Algorithms; Case studies; Collaboration; Consortia; Cooperation; Decision making; Earth science; Feasibility studies; Geographical distribution; Geography; Information sharing; Integration; Interfaces; Internet; Internet service providers; Interoperability; Open access; River networks; rivers; Spatial data; Specifications; Subject specialists; Web services
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi8010012

To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges.