An ArcScene plug-in for volumetric data conversion, modeling and spatial analysis

Shen, Dayong; Wong, David W. S.; Camelli, Fernando; Liu, Yuling

doi:10.1016/j.cageo.2013.08.004

Cited by 11 publications

(1 citation statement)

References 9 publications

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“…Due to the different acquisition means, there are many kinds of data formats to organize spatial vector data, such as Shapefile, KML/KMZ, DXF/DWG, GPX, and others. In spite of OGC having the corresponding documentation standards and interoperability protocols, however, because of the function limitation of processing tools (Shen, Wong, Camelli, & Liu, 2013), it is very difficult to implement the format conversion for large data-sets. Data conversion is the normalization and aggregation from one format to another (Jhummarwala, Mazin, & Potdar, 2016).…”

Section: Data Pre-processingmentioning

confidence: 99%

Big spatial vector data management: a review

Yao

2018

Big Earth Data

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Spatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other datasets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures. To support them more fully, firstly, we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era. Then, we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD, including data storage and organization, spatial index, processing methods, and spatial analysis. Finally, based on the above commentary and related work, several opportunities and challenges are listed as the future research interests and directions for reference.

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Section: Data Pre-processingmentioning

confidence: 99%