Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities

Evans, Michael R.; Oliver, Dev; Yang, KwangSoo; Zhou, Xun; Ali, Reem Y.; Shekhar, Shashi

doi:10.1007/978-94-024-1531-5_8

Cited by 20 publications

(15 citation statements)

References 36 publications

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“…The information such as derived patterns of compactness of relatively higher and lower buildings, can be used by developing big data for urban planners and managers for better planning of future directions of development of cities, as well as decisions on the location of relatively higher and lower buildings, with the knowledge of the current developments. Recently, the need for large scale Lidar data and big spatial data are confirmed in the literature [92][93][94][95][96]. of future directions of development of cities, as well as decisions on the location of relatively higher and lower buildings, with the knowledge of the current developments.…”

Section: Discussionmentioning

confidence: 99%

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Shirowzhan

Sepasgozar

2019

IJGI

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Deriving 3D urban development patterns is necessary for urban planners to control the future directions of 3D urban growth considering the availability of infrastructure or being prepared for fundamental infrastructure. Urban metrics have been used so far for quantification of landscape and land-use change. However, these studies focus on the horizontal development of urban form. Therefore, questions remain about 3D growth patterns. Both 3D data and appropriate 3D metrics are fundamentally required for vertical development pattern extraction. Airborne light detection and ranging (Lidar) as an advanced remote-sensing technology provides 3D data required for such studies. Processing of airborne lidar to extract buildings’ heights above a footprint is a major task and current automatic algorithms fail to extract such information on vast urban areas especially in hilly sites. This research focuses on proposing new methods of extraction of ground points in hilly urban areas using autocorrelation-based algorithms. The ground points then would be used for digital elevation model generation and elimination of ground elevation from classified buildings points elevation. Technical novelties in our experimentation lie in choosing a different window direction and also contour lines for the slant area, and applying moving windows and iterating non-ground extraction. The results are validated through calculation of skewness and kurtosis values. The results show that changing the shape of windows and their direction to be narrow long squares parallel to the ground contour lines, respectively, improves the results of classification in slant areas. Four parameters, namely window size, window shape, window direction and cell size are empirically chosen in order to improve initial digital elevation model (DEM) creation, enhancement of the initial DEM, classification of non-ground points and final creation of a normalised digital surface model (NDSM). The results of these enhanced algorithms are robust for generating reliable DEMs and separation of ground and non-ground points in slant urban scenes as evidenced by the results of skewness and kurtosis. Offering the possibility of monitoring urban growth over time with higher accuracy and more reliable information, this work could contribute in drawing the future directions of 3D urban growth for a smarter urban growth in the Smart Cities paradigm.

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Section: Discussionmentioning

confidence: 99%

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Shirowzhan

Sepasgozar

2019

IJGI

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“…The above mentioned studies address a few of the shortcomings of traditional geoprocessing while some others [25,68] extend the geoprocessing functionality to work upon parallel and distributed processing systems. Beside the complex processing of geospatial data, a considerable amount of work has been done on use of multidimensional data structures in information processing systems (IPS), the applications of which have been in fields of business analysis, astronomy, geomatics, bioinformatics, etc.…”

Section: Big-geospatial Data Processingmentioning

confidence: 99%

Multi-dimensional geospatial data mining in a distributed environment using MapReduce

Alkathiri

Abdul²,

Potdar³

2019

J Big Data

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Data mining and machine learning techniques for processing raster data consider a single spectral band of data at a time. The individual results are combined to obtain the final output. The essence of related multi-spectral information is lost when the bands are considered independently. The proposed platform is based on Apache Hadoop ecosystem and supports performing analysis on large amounts of multispectral raster data using MapReduce. A novel technique of transforming the spectral space to the geometrical space is also proposed. The technique allows to consider multiple bands coherently. The results of clustering 10 6 pixels for multiband imagery with widely used GIS software have been tested and other machine learning methods are planned to be incorporated in the platform. The platform is scalable to support tens of spectral bands. The results from our platform were found to be better and are also available faster due to application of distributed processing.

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“…Ming-H. Tsou in [40] provides a review on the big data application, techniques, and tools in reference to Geo informatics system in geography and GI Science and focuses on fast computing, cloud and grid computing, data mining, machine learning, 2D-3D spatial databases, GPS, GIS and navigation. In [41], authors clarify that there are many spatial datasets introduced on the market, such as global positioning system (GPS), vehicle EM, social, etc. Spatial big data (SBD) refers to the value prepositional dataset that helps find a computational platform.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Aspects of Big Data

Karim

Soomro

Burney

2018

Applied Computer Systems

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Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Frameworks have been compared on the basis of architectural characteristics and feature attributes as well. These two categories project a significant effect on the execution of spatiotemporal data in big data. Frameworks are able to solve the real-time problems in less time of cycle. This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.

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Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities

Cited by 20 publications

References 36 publications

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Multi-dimensional geospatial data mining in a distributed environment using MapReduce

Spatiotemporal Aspects of Big Data

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