Remote sensing data sharing platform for emergency management

2009 ◽  
Author(s):  
Yong Tu ◽  
Jie Peng ◽  
Qi Li ◽  
Cheng Dong ◽  
Hui Zhao
Keyword(s):  
Remote Sensing ◽  
Emergency Management ◽  
Data Sharing ◽  
Remote Sensing Data ◽  
Sensing Data

Implementation of a Federated Large-Scale Remote Sensing Data Sharing Platform

2021 ◽  
Author(s):  
Xuan Ma ◽  
Zhibao Wang ◽  
Lu Bai ◽  
Bingbing Xu ◽  
Juntao Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Sharing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Sensing Data

A Method to Establish 3-D Finite Element Geological Model Based on DEM Data

2014 ◽  
Vol 722 ◽  
pp. 308-311 ◽  
Author(s):  
Ming Zhu Guo ◽  
Feng Xian Zhao ◽  
Ya Kun Wang
Keyword(s):  
Remote Sensing ◽  
Finite Element ◽  
Data Sharing ◽  
Remote Sensing Data ◽  
Scientific Data ◽  
Geological Model ◽  
Ansys Software ◽  
Sensing Data ◽  
Finite Element Software ◽  
Office Software

This paper introduces a methodology for creating geometrically of real terrain based on DEM data, which provided by the international scientific data sharing center.The method is to convert the DEM data in Global Mapper, AutoCAD and other softwares, process batch of data using office software, based on APDL language programming in ANSYS software , so as to realize the real remote sensing data to establish 3-D geological model in finite element software. An example illustrates the capabilities of the proposed methodology in this paper.

scholarly journals A High-Dimensional Indexing Model for Multi-Source Remote Sensing Big Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1314
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Xianqing Tai
Keyword(s):  
Remote Sensing ◽  
Data Sharing ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Remote Sensing Data ◽  
Neighborhood Search ◽  
Continuous Space ◽  
Sensing Data ◽  
Indexing Model ◽  
Spatio Temporal

With continuous improvement of earth observation technology, source, and volume of remote sensing data are gradually enriched. It is critical to realize unified organization and to form data sharing service capabilities for massive remote sensing data effectively. We design a hierarchical multi-dimensional hybrid indexing model (HMDH), to address the problems in underlying organization and management, and improve query efficiency. Firstly, we establish remote sensing data grid as the smallest unit carrying and processing spatio-temporal information. We implement the construction of the HMDH in two steps, data classification based on fuzzy clustering algorithm, and classification optimization based on recursive neighborhood search algorithm. Then, we construct a hierarchical “cube” structure, filled with continuous space filling curves, to complete the coding of the HMDH. The HMDH reduces the amount of data to 6–17% and improves the accuracy to more than eight times than traditional grid model. Moreover, it can reduce the query time to 25% in some query scenarios than algorithms selected as the baseline in this paper. The HMDH model proposed can be used to solve the efficiency problems of fast and joint retrieval of remote sensing data. It extends the pattens of data sharing service and has a high application value.

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