Paper Info
Title
A Land Use/Land Cover Change Geospatial Cyberinfrastructure To Integrate Big Data And Temporal Topology
Abstract
Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data.
Year
DOI
Venue
2016
10.1080/13658816.2015.1104534
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
Field
DocType
LUCC, geospatial cyberinfrastructure, optimization, spatiotemporal object model
Geospatial analysis,Data mining,Raster data,Computer science,Cyberinfrastructure,Image segmentation,Artificial intelligence,Big data,Group method of data handling,Land cover,Machine learning,Land use
Journal
Volume
Issue
ISSN
30
3
1365-8816
Citations 
PageRank 
References 
2
0.36
28
Authors
2
Name
Order
Citations
PageRank
Jin Xing121.04
renee e sieber2332.85