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Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

Authors :
M. Ali Akber Dewan
Mihal Miu
Junye Wang
Xiaokun Zhang
Source :
Big Data and Cognitive Computing; Volume 2; Issue 2; Pages: 9, Big Data and Cognitive Computing, Vol 2, Iss 2, p 9 (2018)
Publication Year :
2021
Publisher :
Ryerson University Library and Archives, 2021.

Abstract

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

Details

Database :
OpenAIRE
Journal :
Big Data and Cognitive Computing; Volume 2; Issue 2; Pages: 9, Big Data and Cognitive Computing, Vol 2, Iss 2, p 9 (2018)
Accession number :
edsair.doi.dedup.....49b103681d9162e3305f75f5cf4f4125
Full Text :
https://doi.org/10.32920/14638815