Paper Info
Title
An On-Demand Retrieval Method Based on Hybrid NoSQL for Multi-Layer Image Tiles in Disaster Reduction Visualization.
Abstract
Monitoring, response, mitigation and damage assessment of disasters places a wide variety of demands on the spatial and temporal resolutions of remote sensing images. Images are divided into tile pyramids by data sources or resolutions and published as independent image services for visualization. A disaster-affected area is commonly covered by multiple image layers to express hierarchical surface information, which generates a large amount of namesake tiles from different layers that overlay the same location. The traditional tile retrieval method for visualization cannot distinguish between distinct layers and traverses all image datasets for each tile query. This process produces redundant queries and invalid access that can seriously affect the visualization performance of clients, servers and network transmission. This paper proposes an on-demand retrieval method for multi-layer images and defines semantic annotations to enrich the description of each dataset. By matching visualization demands with the semantic information of datasets, this method automatically filters inappropriate layers and finds the most suitable layer for the final tile query. The design and implementation are based on a two-layer NoSQL database architecture that provides scheduling optimization and concurrent processing capability. The experimental results reflect the effectiveness and stability of the approach for multi-layer retrieval in disaster reduction visualization.
Year
DOI
Venue
2017
10.3390/ijgi6010008
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
Field
DocType
on-demand,multi-layer,semantic description,NoSQL,disaster reduction visualization
Data mining,Data architecture,Multi layer,Scheduling (computing),Visualization,Computer science,Server,NoSQL,Overlay,Tile
Journal
Volume
Issue
ISSN
6
1
2220-9964
Citations 
PageRank 
References 
0
0.34
28
Authors
5
Name
Order
Citations
PageRank
Linyao Qiu100.34
Qing Zhu2101.29
Zhiqiang Du3445.46
Meng Wang4285.60
Yida Fan570.84