A Map-Reduce-enabledSOLAPcubeforlarge-scaleremotelysensed data aggregation

Spatial On-LineAnalyticalProcessing(SOLAP)isapowerfuldecisionsupportsystemstoolforexploring the multidimensionalperspectiveofspatialdata.Inrecentyears,remotelysenseddatahavebeen integratedintoSOLAPcubes,andthisimprovementhasadvantagesinspatio-temporalanalysisfor environmentmonitoring.However,theperformanceofaggregationsinSOLAPstillfacesaconsiderable challenge fromthelarge-scaledatasetgeneratedbyEarthobservation.Fromtheperspectiveofdata parallelism, atile-basedSOLAPcubemodel,theso-calledTileCube,ispresentedinthispaper.Thenovel model implementsRoll-Up/Drill-AcrossoperationsintheSOLAPenvironmentbasedonMap-Reduce,a popular data-intensivecomputingparadigm,andimprovesthethroughputandscalabilityofraster aggregation. Therefore,thelongtime-series,wide-rangeandmulti-viewanalysisofremotelysensed data canbeprocessedinashorttime.TheTileCubeprototypewasbuiltonHadoop/Hbase,anddrought monitoring isusedasanexampletoillustratetheaggregationsinthemodel.Theperformancetesting indicated themodelcanbescaledalongwithboththedatagrowthandnodegrowth.Itisapplicableand natural tointegratetheSOLAPcubewithMap-Reduce.Factorsthatinfluence theperformancearealso discussed, andthebalanceofthemwillbeconsideredinfutureworkstomakefulluseofdatalocalityfor model optimisation

Optimized Route Selection Method based on the Turns of Road Intersections: A Case Study on Oversized Cargo Transportation

For oversized cargo transportation, traditional transportation schemes only consider road length, road width, the transportation cost as weight values in analysis and calculation of route selection. However, for oversized trucks, turning direction at road intersections is also a factor worth considering. By introducing the classical algorithm of Dijkstra into the model of road network, this research considers the size of turning angle at intersections as the weight value of the edge in the auxiliary network based on the weight values of road corners, upon which the shortest path analysis is performed. Then, an optimal path with minimum time cost was eventually obtained. The proposed algorithm was analyzed and compared with the traditional shortest path algorithm and it reported that our method could reduce the time for oversized trucks to pass through intersections. In addition, the proposed algorithm could be adapted to the complex and diverse road networks and provide a reliable scheme for route selection of oversized trucks

Improved Orthogonal T-Snake Model for Complex Water Boundary Extraction

A topology adaptive snake (T-Snake) model based on orthogonal grids is introduced and improved in this paper, and a proper energy function is designed. A detection and handling mechanism for topological conflict that caused by island shaped hollow is proposed in the model, and therefore accurate extraction for complex boundary of river containing river islands is achieved. For the disadvantage of the need to manually construct the initial contour in the orthogonal T-Snake model, using the minimum fractal dimension to obtain one area of the water and automatically generate an initial contour. The experiment shows that the algorithm of this paper can accurately extract the boundary of the complex water which contains deeply concave regions or river islands, and it has higher accuracy and less time cost than classic Snake model and GVF Snake model

Using Cloud Computing to Accelerate Large Spatial Data Sharing

Attempting to share or access spatial data across the Internet proves to be a frustrating and time-consuming experience. To provide a âlocal-likeâ performance, a WAN/cloud-optimized protocol known as âCloudJetâ developed at our lab was used as the underlying multi-stream/multi-path engine. The results demonstrate that it's capable to deal with long-distance, cross-domain spatial data access and transfer up to several times

Relational Database Extension Oriented, Self-adaptive Imagery Pyramid Model

With the development of remote sensing technology, especially the improvement of sensor resolution, the amount of image data is increasing. This puts forward higher requirements to manage huge amount of data efficiently and intelligently. And how to access massive remote sensing data with efficiency and smartness becomes an increasingly popular topic. In this paper, against current development status of Spatial Data Management System, we proposed a self-adaptive strategy for image blocking and a method for LoD(level of detail)model construction that adapts, with the combination of database storage, network transmission and the hardware of the client. Confirmed by experiments, this imagery management mechanism can achieve intelligent and efficient storage and access in a variety of different conditions of database, network and client. This study provides a feasible idea and method for efficient image data management, contributing to the efficient access and management for remote sensing image data which are based on database technology under network environment of C/S architecture