Bedform Mapping: Multibeam Data Processing, Metadata and Spatial Data Services

2016 ◽  
pp. 3-6
Author(s):  
S. Diez ◽  
J. Sorribas
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Data Services ◽  
Multibeam Data

Infrastructural approach to processing of spatial data in the problems of management of territorial development

2018 ◽  
Author(s):  
И.В. Бычков ◽  
Г.М. Ружников ◽  
В.В. Парамонов ◽  
А.С. Шумилов ◽  
Р.К. Фёдоров
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Data Exchange ◽  
Complex Analysis ◽  
Data Entry ◽  
Content Management ◽  
Volume Data ◽  
Territorial Development ◽  
Distributed Services ◽  
Spatial Data Processing

Рассмотрен инфраструктурный подход обработки пространственных данных для решения задач управления территориальным развитием, который основан на сервис-ориентированной парадигме, стандартах OGC, web-технологиях, WPS-сервисах и геопортале. The development of territories is a multi-dimensional and multi-aspect process, which can be characterized by large volumes of financial, natural resources, social, ecological and economic data. The data is highly localized and non-coordinated, which limits its complex analysis and usage. One of the methods of large volume data processing is information-analytical environments. The architecture and implementation of the information-analytical environment of the territorial development in the form of Geoportal is presented. Geoportal provides software instruments for spatial and thematic data exchange for its users, as well as OGC-based distributed services that deal with the data processing. Implementation of the processing and storing of the data in the form of services located on distributed servers allows simplifying their updating and maintenance. In addition, it allows publishing and makes processing to be more open and controlled process. Geoportal consists of following modules: content management system Calipso (presentation of user interface, user management, data visualization), RDBMS PostgreSQL with spatial data processing extension, services of relational data entry and editing, subsystem of launching and execution of WPS-services, as well as services of spatial data processing, deployed at the local cloud environment. The presented article states the necessity of using the infrastructural approach when creating the information-analytical environment for the territory management, which is characterized by large volumes of spatial and thematical data that needs to be processed. The data is stored in various formats and applications of service-oriented paradigm, OGC standards, web-technologies, Geoportal and distributed WPS-services. The developed software system was tested on a number of tasks that arise during the territory development.

scholarly journals UAV and terrestrial laser scanner data processing for large scale topographic mapping

2020 ◽  
Vol 50 ◽  
pp. 63-73
Author(s):  
Ganbold Ulziisaikhan ◽  
Dash Oyuntsetseg
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Large Scale ◽  
Laser Scanner ◽  
Measurement Technology ◽  
Topographic Mapping ◽  
Terrestrial Laser Scanner ◽  
Scanner Data ◽  
Processing Scheme ◽  
Digital Elevation

Integrating spatial data from different sources results in visualization, which is the last step in the process of digital basic topographic map creation. Digital elevation model and visualization will used for geomorphological mapping, geospatial database, urban planning and etc. Large scale topographic mapping in the world countries is really a prominent challenge in geospatial industries today. The purpose of this work is to integrate laser scanner data with the ones generated by aerial photogrammetry from UAV, to produce detailed maps that can used by geodetic engineers to optimize their analysis. In addition, terrestrial - based LiDAR scans and UAV photogrammetric data were collected in Sharga hill in the north zone of Mongolia. In this paper, different measurement technology and processing software systems combined for topographic mapping in the data processing scheme. UTM (Universal Transverse Mercator) projected coordinate system calculated in WGS84 reference ellipsoid. Feature compilation involving terrestrial laser scanner data and UAV data will integrated to offer Digital Elevation Models (DEM) as the main interest of the topographic mapping activity. Used UAV generate high-resolution orthomosaics and detailed 3D models of areas where no data, are available. That result issued to create topographic maps with a scale of 1:1000 of geodetic measurements. Preliminary results indicate that discontinuity data collection from UAV closely matches the data collected using laser scanner.

scholarly journals DEVELOPMENT OF A HETEROGENIC DISTRIBUTED ENVIRONMENT FOR SPATIAL DATA PROCESSING USING CLOUD TECHNOLOGIES

2016 ◽  
Vol XLI-B4 ◽  
pp. 385-390
Author(s):  
A. S. Garov ◽  
I. P. Karachevtseva ◽  
E. V. Matveev ◽  
A. E. Zubarev ◽  
I. V. Florinsky
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
3D Visualization ◽  
New Technology ◽  
Main Idea ◽  
Remote Sensing Data ◽  
Data Access ◽  
Working Environment ◽  
Software Environment ◽  
Spatial Data Processing

We are developing a unified distributed communication environment for processing of spatial data which integrates web-, desktop- and mobile platforms and combines volunteer computing model and public cloud possibilities. The main idea is to create a flexible working environment for research groups, which may be scaled according to required data volume and computing power, while keeping infrastructure costs at minimum. It is based upon the "single window" principle, which combines data access via geoportal functionality, processing possibilities and communication between researchers. Using an innovative software environment the recently developed planetary information system (<a href="http://cartsrv.mexlab.ru/geoportal"target="_blank">http://cartsrv.mexlab.ru/geoportal</a>) will be updated. The new system will provide spatial data processing, analysis and 3D-visualization and will be tested based on freely available Earth remote sensing data as well as Solar system planetary images from various missions. Based on this approach it will be possible to organize the research and representation of results on a new technology level, which provides more possibilities for immediate and direct reuse of research materials, including data, algorithms, methodology, and components. The new software environment is targeted at remote scientific teams, and will provide access to existing spatial distributed information for which we suggest implementation of a user interface as an advanced front-end, e.g., for virtual globe system.

scholarly journals Application of NEXRAD Radar-Based Quantitative Precipitation Estimations for Hydrologic Simulation Using ArcPy and HEC Software

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 273
Author(s):  
Younghyun Cho
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Performance Test ◽  
Hydrological Modeling ◽  
Rainfall Data ◽  
Storm Event ◽  
Rainfall Runoff ◽  
Hydrologic Simulation ◽  
Great Opportunity ◽  
Spatially Distributed

Recent availability of various spatial data, especially for gridded rainfall amounts, provide a great opportunity in hydrological modeling of spatially distributed rainfall–runoff analysis. In order to support this advantage using gridded precipitation in hydrological application, (1) two main Python script programs for the following three steps of radar-based rainfall data processing were developed for Next Generation Weather Radar (NEXRAD) Stage III products: conversion of the XMRG format (binary to ASCII) files, geo-referencing (re-projection) with ASCII file in ArcGIS, and DSS file generation using HEC-GridUtil (existing program); (2) eight Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) models of ModClark and SCS Unit Hydrograph transform methods for rainfall–runoff flow simulations using both spatially distributed radar-based and basin-averaged lumped gauged rainfall were respectively developed; and (3) three storm event simulations including a model performance test, calibration, and validation were conducted. For the results, both models have relatively high statistical evaluation values (Nash–Sutcliffe efficiency—ENS 0.55–0.98 for ModClark and 0.65–0.93 for SCS UH), but it was found that the spatially distributed rainfall data-based model (ModClark) gives a better fit regarding observed streamflow for the two study basins (Cedar Creek and South Fork) in the USA, showing less requirements to calibrate the model with initial parameter values. Thus, the programs and methods developed in this research possibly reduce the difficulties of radar-based rainfall data processing (not only NEXRAD but also other gridded precipitation datasets—i.e., satellite-based data, etc.) and provide efficiency for HEC-HMS hydrologic process application in spatially distributed rainfall–runoff simulations.

Research on Spatial Statistical Data Compression Algorithm Based on Point Cloud Clustering

2014 ◽  
Vol 543-547 ◽  
pp. 1619-1622
Author(s):  
Shao Ming Pan ◽  
Hong Li ◽  
Ge Tang
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Statistical Data ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Information Service ◽  
Compression Algorithm ◽  
Compression Efficiency ◽  
Data Services ◽  
Run Length Coding

The spatial information service system have the characteristics of a large amount of massive data and a large number of users, so the compression algorithm for statistical data will affects the system performance of spatial data services. A compression algorithm for spatial statistical data based on point cloud clustering is proposed in this paper to solve the above-mentioned problems. The access rule of the spatial data will be mapped into a point cloud. And the part of points will be deleted according to the clustering gradient of point cloud. Then the statistical data is compressed by run-length coding and spatial clustering extraction. The experimental results show that the algorithm have a high compression efficiency.

A WebGIS for Spatial Data Processing, Analysis, and Distribution for the MER 2003 Mission

2007 ◽  
Vol 73 (6) ◽  
pp. 671-680 ◽  
Author(s):  
Rongxing Li ◽  
Kaichang Di ◽  
Jue Wang ◽  
Xutong Niu ◽  
Sanchit Agarwal ◽  
...  
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Spatial Data Processing
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