Abstract
Large-scale geospatial data have accumulated worldwide in the past decades. However, various data formats often result in a geospatial data sharing problem in the geographical information system community. Despite the various methodologies proposed in the past, geospatial data conversion has always served as a fundamental and efficient way of sharing geospatial data. However, these methodologies are beginning to fail as data increase. This study proposes a parallel spatial data conversion engine (PSCE) with a symmetric mechanism to achieve the efficient sharing of massive geodata by utilizing high-performance computing technology. This engine is designed in an extendable and flexible framework and can customize methods of reading and writing particular spatial data formats. A dynamic task scheduling strategy based on the feature computing index is introduced in the framework to improve load balancing and performance. An experiment is performed to validate the engine framework and performance. In this experiment, geospatial data are stored in the vector spatial data defined in the Chinese Geospatial Data Transfer Format Standard in a parallel file system (Lustre Cluster). Results show that the PSCE has a reliable architecture that can quickly cope with massive spatial datasets.
Highlights
The current tools and equipment for capturing geospatial data at both mega and milli scales are insufficient
This study proposes a novel parallel spatial data conversion engine (PSCE) to achieve the efficient sharing of massive geodata especially in vector format by utilizing high-performance computing technics
Spatial data conversion techniques have gone through three generations
Summary
The current tools and equipment for capturing geospatial data at both mega and milli scales are insufficient. The constant improvement of distributed computing, networks, and middleware technology enables GIS practitioners to develop complicated and powerful mediator-based systems, allowing them to share data in a large scale. Such systems were designed to be constructed from numerous and relatively autonomous sources of data and services, which could communicate with one another over a standard protocol, enabling users to issue a single query to the mediator and provide the capability to return a result that seamlessly combines information from multiple sources [15,21,22]. This study proposes a novel parallel spatial data conversion engine (PSCE) to achieve the efficient sharing of massive geodata especially in vector format by utilizing high-performance computing technics.
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