Relevant bibliographies by topics / Geospatial data Environment

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Geospatial data Environment'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Geospatial data Environment"

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Charvat, Karel, Otakar Cerba, Dmitrij Kozuch, and Marek Splichal. "Geospatial Data Based Environment in INSPIRE4Youth." Procedia Computer Science 104 (2017): 183–89. http://dx.doi.org/10.1016/j.procs.2017.01.101.

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PAUL, MANOJ, and S. K. GHOSH. "A SERVICE-ORIENTED APPROACH FOR INTEGRATING HETEROGENEOUS SPATIAL DATA SOURCES REALIZATION OF A VIRTUAL GEO-DATA REPOSITORY." International Journal of Cooperative Information Systems 17, no. 01 (March 2008): 111–53. http://dx.doi.org/10.1142/s0218843008001774.

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Searching and accessing geospatial information in the open and distributed environments of geospatial information systems poses several challenges due to the heterogeneity in geospatial data. Geospatial data is highly heterogeneous — both at the syntactic and semantic level. The requirement for an integration architecture for seamless access of geospatial data has been raised over the past decades. The paper proposes a service-based model for geospatial integration where each geospatial data provider is interfaced on the web as services. The interface for these services has been described with Open Geospatial Consortium (OGC) specified service standards. Catalog service provides service descriptions for the services to be discovered. The semantic of each service description is captured in the form of ontology. The similarity assessment method of request service with candidate services proposed in this paper is aimed at resolving the heterogeneity in semantics of locational terms of service descriptions. In a way, we have proposed an architecture for enterprise geographic information system (E-GIS), which is an organization-wide approach to GIS integration, operation, and management. A query processing mechanism for accessing geospatial information in the service-based distributed environment has also been discussed with the help of a case study.
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Ronzhin, S., G. Bosch, E. Folmer, and R. Lemmens. "LINKDALE: A LIGHTWEIGHT LEARNING ENVIRONMENT FOR (GEOSPATIAL) LINKED DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 187–90. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-187-2018.

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<p><strong>Abstract.</strong> Modern software tools for managing Linked Data are often designed for skilled users. Therefore, they cannot be used for education purposes because they require substantial a priori knowledge about the Resource Description Framework and the SPARQL query language. LinkDaLe is a single page application designed to teach students the concept of Linked Data and work with linked data at the same time. In the paper we showcase the interface and functionality of LinkDaLe by triplifying data on Geo4All member organizations. The application was built and evaluated within The Business Process Integration Lab, a master programme course in 2016 and 2017 years. Positive feedback from both students and teachers proved the relevance of the proposed design consideration. LinkDaLe showed usability working with domain specific data e.g. geospatial and logistic data.</p>
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Lee, Ahyun, Yoon-Seop Chang, and Insung Jang. "Planetary-Scale Geospatial Open Platform Based on the Unity3D Environment." Sensors 20, no. 20 (October 21, 2020): 5967. http://dx.doi.org/10.3390/s20205967.

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Digital twin technology based on building a virtual digital city similar to a real one enables the simulation of urban phenomena or the design of a city. A geospatial platform is an essential supporting component of digital twin cities. In this study, we propose a planetary-scale geospatial open platform that can be used easily in the most widely used game engine environment. The proposed platform can visualize large-capacity geospatial data in real time because it organizes and manages various types of data based on quadtree tiles. The proposed rendering tile decision method provides constant geospatial data visualization according to the camera controls of the user. The platform implemented is based on Unity3D, and therefore, one can use it easily by importing the proposed asset library. The proposed geospatial platform is available on the Asset Store. We believe that the proposed platform can meet the needs of various three-dimensional (3-D) geospatial applications.
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Olasz, A., D. Kristóf, B. Nguyen Thai, M. Belényesi, and R. Giachetta. "PROCESSING BIG REMOTE SENSING DATA FOR FAST FLOOD DETECTION IN A DISTRIBUTED COMPUTING ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W2 (July 5, 2017): 137–38. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w2-137-2017.

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The Earth observation (EO) missions of the space agencies and space industry (ESA, NASA, national and commercial companies) are evolving as never before. These missions aim to develop and launch next-generation series of satellites and sensors and often provide huge amounts of data, even free of charge, to enable novel monitoring services. The wide geospatial sector is targeted to handle new challenges to store, process and visualize these geospatial data, reaching the level of Big Data by their volume, variety, velocity, along with the need of multi-source spatio-temporal geospatial data processing. Handling and analysis of remote sensing data has always been a cumbersome task due to the ever-increasing size and frequency of collected information. This paper presents the achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of geospatial big data in the context of flood and waterlogging detection.
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Heldens, Wieke, Cornelia Burmeister, Farah Kanani-Sühring, Björn Maronga, Dirk Pavlik, Matthias Sühring, Julian Zeidler, and Thomas Esch. "Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing." Geoscientific Model Development 13, no. 11 (November 27, 2020): 5833–73. http://dx.doi.org/10.5194/gmd-13-5833-2020.

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Abstract. The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized format. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This article shows how input data sets for three German cities were derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial data.
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Zheng, Feng, Han Rong Lu, Yan An, and Le Jiang Guo. "The Research and Implementation of Geospatial Data Management Based on ArcGIS Service." Advanced Materials Research 605-607 (December 2012): 2379–82. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2379.

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The main objective of this paper is to design and develop a basis geospatial data management system. It offers database building, conversion, topology check, query statistics, data extraction, cartographic output, analysis and decision-making for massive geospatial data on a range of basic geospatial data management function. The main research includes the design of basis geospatial database, data management system and the key technologies in system development. Geographic information from 2D to 3D is the result of technical progress and demand-driven, 3D modeling and 3D visualization environment-based spatial analysis and query is the future development direction of the geospatial data management system.
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Karthi, S., and S. Prabu. "Secure geospatial data storage using SpatialHadoop framework in cloud environment." International Journal of Internet Technology and Secured Transactions 8, no. 4 (2018): 516. http://dx.doi.org/10.1504/ijitst.2018.095926.

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Karthi, S., and S. Prabu. "Secure geospatial data storage using SpatialHadoop framework in cloud environment." International Journal of Internet Technology and Secured Transactions 8, no. 4 (2018): 516. http://dx.doi.org/10.1504/ijitst.2018.10017026.

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Zhai, X., L. Jiang, and P. Yue. "Web-Based Geospatial Resource Sharing Through GeoPW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 131–35. http://dx.doi.org/10.5194/isprsarchives-xl-6-131-2014.

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As Web-related technologies have matured in recent years, an increasing amount of geospatial resources (e.g. geospatial services, workflows, and geospatial data) are available in the distributed Web environment. Consequently, effective and efficient sharing and management of geospatial resources on the Web are necessary for better utilizing these resources for education and scientific research. This matches the vision of Geoprocessing Web, which emphasizes the sharing and access of geoprocessing utilities from the perspectives of communication, collaboration, and participation. Previous work on GeoPW has provided a large number of geoprocessing services over the Web. In this paper, GeoPW goes further to offer a Web platform for sharing geospatial resources. The paper presents the design, implementation, and functions of the platform, which offers a user-friendly environment for publication, discovery, and communication of geospatial data, services, and workflows.
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Dissertations / Theses on the topic "Geospatial data Environment"

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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.
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Wilson, R. "Quantifying Himalayan glacier change from the 1960s to early 2000s, using corona, glims and aster geospatial data." Thesis, University of Salford, 2015. http://usir.salford.ac.uk/35932/.

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Since reaching their LIAMs, Himalayan glaciers have generally undergone a period of retreat, evident from large moraines left at former ice limits. Currently, however, detailed assessments of Himalayan glacier fluctuations over the past century are limited and fail to compare spatially or temporally to records available in Central Europe, North America and Scandinavia. Consequently, the variability and magnitude of glacial change across the Himalayas, which is a key indicator of climatic change in this region, is yet to be fully understood. Against a background of poor data availability, Corona imagery and historic GLIMS glacier outlines now offer an opportunity to assess glacier extent for regions of the Himalayas pre-1980. Corona imagery, acquired by a US space-borne reconnaissance mission operational from 1960 to 1970, represents a particularly unique dataset offering high resolution imagery (~1.8 m) with stereo-scopic capabilities. Utilising Corona imagery, there is an opportunity to produce detailed maps of Himalayan glacier extent and extract ice surface elevation estimations, in some instances, for the first time. Despite having been de-classified in 1995, the use of Corona data in the Himalayas has been neglected, mainly because of orthorectification challenges related to its unique geometric distortions. Hence, there remains a need to develop a low cost and easily replicable method of accurately orthorectifying Corona imagery enabling its use as a large-scale glacier mapping tool in the Himalayas. In response to this need, Corona images are orthorectified in this study through the use of: (1) a non-metric photogrammetry approach; and (2) horizontal and vertical reference data acquired from ortho-ASTER imagery and the freely available ASTER GDEM. By comparing glacier measurements derived from Corona imagery, GLIMS data and more contemporary ASTER data, changes in glacier area, length and in some instances volume, between the 1960/70s and early 2000s, were quantified for glaciers selected within four study areas located in Uttarakhand, India and Central Nepal. Importantly, this cross-regional glacier change dataset both complements and enhances current Himalayan records. Most notably, results indicate that glaciers selected in the Bhagirathi and Pindar/Kali basins, Uttarakhand, reduced in area by a relatively small 7.97±0.29% and 7.54±0.26%, respectively. Contrastingly, glaciers selected in the more easterly located Seti and Trisula basins reduced in area by 29.78±0.2% and 50.55±0.08%, respectively. Comparisons of Corona DEM (derived from Corona stereo-pairs) and ASTER Global DEM elevations at the terminus regions of four glaciers revealed extensive surface lowering, ranging from 87±27 m to 142±27 m. For Corona processing, the methods applied were shown to orthorectify Corona images to an accuracy that allows comparable glacier outlines to be delineated, further demonstrating the mapping potential of this dataset. However, for Corona DEM extraction, the use of ASTER spatial control data was shown to be inadequate and the presence of large vertical errors in the DEMs generated hindered the measurement of glacier volume change. For this purpose, it is therefore recommended that the methods developed are tested with the use of very high resolution spatial control data.
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Abdul, Majeed Zainal. "From geospatial data capture to the delivery of GIS-ready information : improved management within a GIS environment." Thesis, University of Newcastle Upon Tyne, 2006. http://hdl.handle.net/10443/680.

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This thesis presents the research undertaken to investigate how geospatial data handling techniques and technology can be potentially used to enhance the existing management of entire survey datasets from their captured stage to a GIS-ready state and the delivery of this to the user. Discovery of the current systems for managing survey data and information in the Survey and Mapping Department Malaysia (JUPEM) has been presented. In addition, the surveying practice and processes carried out have been examined, especially the different type of data and information existed within the raw data capture right through to the production of GIS-ready information. The current GIS technology and techniques for managing geospatial data have been inspected to gain an in-depth understanding of them. Geospatial object as an approach to model reality of the world has been discovered and used to model the raw, processed, the GIS-ready information. To implement the management, a prototype Database Management System (DBMS) has been implemented, and a trial data population and processing steps have been carried out. An enhancement of the managemenot f the datasetsf rom geospatiald ata capturet o the GIS-ready infori-nation has beend emonstratedT. o deliver online the final product, demonstrationo f available methods were illustrated, and then contrasted. A range of datasets around Malaysian context were used in the research. The investigation revealed that raw, processed and GIS-ready information can be successfully modelled as object in an object-relational spatial database. Using inherent GIS tools, survey datasets management and processing steps within the same system are evidently achieved in a prototype implemented DBMS. An improved management showing the capability of 'drill-down search' and 'two-way traceability' to access and search spatial and non-spatial information in the system is effectively illustrated. Demonstration of the vendor specific and open source technology for the GIS-ready information delivery leads to the comparison between them. The thesis concludes by recognising that a management for raw captured data, processed set of data and GIS-ready information, and the delivery of this, within GIS environment is possible. The inherent GIS tools and DBMS have presented a single-view system for geospatial data management providing superior interfaces that are easy to learn and use, and users are able to specify and perform the desired tasks efficiently. Delivery of data has some constraints that need to be considered before embarking into either vendor specific application or open source technology. In JUPEM, time and cost can be reduced by applying and implementing the suggested GIS application for cadastral and topographic surveys right up to the creation of GIS-ready information, as detailed in the thesis. The research also finds that the in-depth understanding and experience, practically and theoretically, of all aspects of current GIS technologies and techniques gained through this research has achieved an overarching inspiration: equalisation of a high level of awareness and ability of staff in handling GIS project development within currently developing countries with those in the developed countries, and within the national survey and mapping department with those of other government departments and commercial GIS contractors.
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Wodajo, Bikila Teklu. "Geospatial analysis of spaceborne remote sensing data for assessing disaster impacts and modeling surface runoff in the built-environment /." Full text available from ProQuest UM Digital Dissertations, 2009. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=0&did=1798970281&SrchMode=1&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1269359779&clientId=22256.

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Thesis (Ph.D.)--University of Mississippi, 2009.
Typescript. Vita. "May 2009." Committee director: Waheed Uddin Includes bibliographical references (leaves 329-344). Also available online via ProQuest to authorized users.
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Rathnayaka, Mudiyanselage Udara Madushantha Somarathna. "Data quality analysis in a GIS environment of OpenStreetMap geodatabase for Sri Lanka." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The purpose of the present study is to analyze the data quality of OpenStreetMap geodatabase in a GIS environment; the case of study is a region of Sri Lanka. OpenStreetMap (OSM) is one of the most well-known crowd-sourced products, providing a global map base thanks to the mapping activity carried out by volunteers all around the world. As the quality of collected information remains a significant concern for the geospatial information community and in geospatial data management, a qualitative and quantitative assessment of OSM data is of great importance, due to the large diffusion and adoption of this kind of volunteered geospatial information (VGI). This study concerns the OSM dataset currently available for the Mawanella area in Sri Lanka and has been performed in an open-source Geographic Information System (GIS) environment, QGIS. OSM vector files are the raw materials for the analysis. The evaluation has been realized considering the main quality attributes to be maintained in a mapping product, either based on intrinsic properties and on the relationship with official databases available for the same area. The results of the study suggest that the current quality of OSM maps in the study area is fairly good, but completeness is poor and must be improved.
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Brazenor, Clare. "The spatial dimensions of Native Title." Connect to thesis, 2000. http://eprints.unimelb.edu.au/archive/00001050.

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Shimazaki, Hiroto. "Application-oriented approaches of geospatial data analysis : case studies on global environmental problems." 京都大学 (Kyoto University), 2009. http://hdl.handle.net/2433/126501.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(工学)
甲第14926号
工博第3153号
新制||工||1473(附属図書館)
27364
UT51-2009-M840
京都大学大学院工学研究科都市環境工学専攻
(主査)教授 田村 正行, 准教授 立川 康人, 准教授 須﨑 純一
学位規則第4条第1項該当
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Santana, Abel. "Geospatial sensor integration| Design of an automated collection, management and visualization system for water quality data." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10007418.

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Water resources have become an increasingly important topic in the world of resource management. Researchers are constantly looking for ways to make the process of data collection, analyzation and visualization more efficient. The current methods involve a multi-step process that is cumbersome and require a lot of man-hours from start to finish. The cost of water quality sensors is also an issue for researchers. Many sensors on the market today range from the $1,000 to $10,000 range making it difficult for small research firms and non profits to conduct water quality monitoring projects. This project attempts to construct a water quality-monitoring platform that will automate the process of collecting, analyzing and visualization water quality data. Special attention will be given to the data visualization aspect of the process with an emphasis on web mapping applications.

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Hanley, John P. "A New Evolutionary Algorithm For Mining Noisy, Epistatic, Geospatial Survey Data Associated With Chagas Disease." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/727.

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The scientific community is just beginning to understand some of the profound affects that feature interactions and heterogeneity have on natural systems. Despite the belief that these nonlinear and heterogeneous interactions exist across numerous real-world systems (e.g., from the development of personalized drug therapies to market predictions of consumer behaviors), the tools for analysis have not kept pace. This research was motivated by the desire to mine data from large socioeconomic surveys aimed at identifying the drivers of household infestation by a Triatomine insect that transmits the life-threatening Chagas disease. To decrease the risk of transmission, our colleagues at the laboratory of applied entomology and parasitology have implemented mitigation strategies (known as Ecohealth interventions); however, limited resources necessitate the search for better risk models. Mining these complex Chagas survey data for potential predictive features is challenging due to imbalanced class outcomes, missing data, heterogeneity, and the non-independence of some features. We develop an evolutionary algorithm (EA) to identify feature interactions in "Big Datasets" with desired categorical outcomes (e.g., disease or infestation). The method is non-parametric and uses the hypergeometric PMF as a fitness function to tackle challenges associated with using p-values in Big Data (e.g., p-values decrease inversely with the size of the dataset). To demonstrate the EA effectiveness, we first test the algorithm on three benchmark datasets. These include two classic Boolean classifier problems: (1) the "majority-on" problem and (2) the multiplexer problem, as well as (3) a simulated single nucleotide polymorphism (SNP) disease dataset. Next, we apply the EA to real-world Chagas Disease survey data and successfully archived numerous high-order feature interactions associated with infestation that would not have been discovered using traditional statistics. These feature interactions are also explored using network analysis. The spatial autocorrelation of the genetic data (SNPs of Triatoma dimidiata) was captured using geostatistics. Specifically, a modified semivariogram analysis was performed to characterize the SNP data and help elucidate the movement of the vector within two villages. For both villages, the SNP information showed strong spatial autocorrelation albeit with different geostatistical characteristics (sills, ranges, and nuggets). These metrics were leveraged to create risk maps that suggest the more forested village had a sylvatic source of infestation, while the other village had a domestic/peridomestic source. This initial exploration into using Big Data to analyze disease risk shows that novel and modified existing statistical tools can improve the assessment of risk on a fine-scale.
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Ruby, Caitlin A. "Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments." Thesis, Mississippi State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10268275.

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The Coastal and Marine Ecological Classification Standard (CMECS) provides a comprehensive framework of common terminology for organizing physical, chemical, biological, and geological information about marine ecosystems. Federally endorsed as a dynamic content standard, all federally funded data must be compliant by 2018; however, applying CMECS to deep sea datasets and underwater video have not been extensively examined. The presented research demonstrates the extent to which CMECS can be applied to deep sea benthic habitats, assesses the feasibility of applying CMECS to remotely operated vehicle (ROV) video data in near-real-time, and establishes best practices for mapping environmental aspects and observed deep sea habitats as viewed by the ROV’s forward-facing camera. All data were collected during 2014 in the Northern Gulf of Mexico by the National Oceanic and Atmospheric Administration’s (NOAA) ROV Deep Discoverer and ship Okeanos Explorer.

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Books on the topic "Geospatial data Environment"

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Hoalst-Pullen, Nancy, and Mark W. Patterson. Geospatial technologies in environmental management. Dordrecht: Springer, 2010.

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Maantay, Juliana. Geospatial Analysis of Environmental Health. Dordrecht: Springer Science+Business Media B.V., 2011.

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National Research Council (U.S.). Committee on Archiving and Accessing Environmental and Geospatial Data at NOAA. Preliminary principles and guidelines for archiving environmental and geospatial data at NOAA: Interim report. Washington, D.C: National Academies Press, 2006.

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Cartwright, William. Headway in Spatial Data Handling: 13th International Symposium on Spatial Data Handling. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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Amicis, Raffaele De. Geospatial visual analytics: Geographical information processing and visual analytics for environmental security. New York: Springer, 2009.

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United States. Department of Commerce. Technical considerations for use of geospatial data in sea level change mapping and assessment. Silver Spring, Maryland]: U.S. Department of Commerce, National Oceanic and Atomospheric Administration, National Ocean Service, 2010.

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Foody, Giles, Peter Mooney, Cidália Costa Fonte, Ana Maria Olteanu Raimond, Steffen Fritz, and Linda See, eds. Mapping and the Citizen Sensor. London, United Kingdom: Ubiquity Press, 2017.

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Shu ju ku, feng xian di tu yu wang luo ping tai. Beijing: Ke xue chu ban she, 2011.

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Lakshmanan, Valliappa. Automating the Analysis of Spatial Grids: A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications. Dordrecht: Springer Netherlands, 2012.

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International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining (2009 Wuhan, China). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining: 13-14 October 2009, Wuhan, China. Edited by Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu, and SPIE (Society). Bellingham, Wash: SPIE, 2009.

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Book chapters on the topic "Geospatial data Environment"

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Vaniš, Pavel. "Data Access for Environment Protection and Economic Development." In GeoSpatial Visual Analytics, 451–64. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0_36.

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Lukin, Vladimir. "Processing of Multichannel Remote Sensing Data for Environment Monitoring." In GeoSpatial Visual Analytics, 129–38. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0_10.

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Han, Jingjing, Mingyu Liu, Genlin Ji, Bin Zhao, Richen Liu, and Ying Li. "Efficient Semantic Enrichment Process for Spatiotemporal Trajectories in Geospatial Environment." In Web and Big Data, 342–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60290-1_27.

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Hu, Mingyuan, Hui Lin, Bingli Xu, Ya Hu, Sammy Tang, and Weitao Che. "Hierarchical Geospatial Computing Environment for Data-intensive Geographic Process Simulation." In Geospatial Techniques for Managing Environmental Resources, 15–32. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1858-6_2.

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Anand, Anupam, and Geeta Batra. "Using big data and geospatial approaches in evaluating environmental interventions." In Evaluating Environment in International Development, 79–92. 2nd ed. 2. | Milton Park, Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9781003094821-7.

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Triplet, Thomas, and Samuel Foucher. "Clustering of Geospatial Big Data in a Distributed Environment." In Encyclopedia of GIS, 1–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23519-6_1625-1.

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Triplet, Thomas, and Samuel Foucher. "Clustering of Geospatial Big Data in a Distributed Environment." In Encyclopedia of GIS, 236–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_1625.

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Kulawiak, Marcin, and Marek Moszynski. "Integration of Geographic Information Systems for Monitoring and Dissemination of Marine Environment Data." In Geospatial Techniques for Managing Environmental Resources, 33–52. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1858-6_3.

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Li, Zhenlong. "Geospatial Big Data Handling with High Performance Computing: Current Approaches and Future Directions." In Geotechnologies and the Environment, 53–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47998-5_4.

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Plue, Reilley, Lauren Jewett, and Michael J. Widener. "Considerations When Using Individual GPS Data in Food Environment Research: A Scoping Review of ‘Selective (Daily) Mobility Bias’ in GPS Exposure Studies and Its Relevance to the Retail Food Environment." In Geospatial Technologies for Urban Health, 95–112. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19573-1_6.

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Conference papers on the topic "Geospatial data Environment"

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Praveen, P., Ch Jayanth Babu, and B. Rama. "Big data environment for geospatial data analysis." In 2016 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2016. http://dx.doi.org/10.1109/cesys.2016.7889816.

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Guerrero, Hugo. "Integrating GIS and Data Management Within the Project Environment." In 2008 7th International Pipeline Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ipc2008-64215.

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Today, much of the focus on integrating Geospatial technology and data has been on the operations side of the business. Not much attention has been paid to the workflow within the project environment even though most of the data that is used to populate enterprise datasets is created or prepared as a requirement of a project; that said; it is early on at the project level when geospatial integration needs to be implemented and incorporated into the project workflow. On the other hand, project teams have historically focused on strictly satisfying the needs of the project. This is typically limited to the minimum work required to design, permit & build a given work scope. This approach has left many companies with the task of paying high costs for the project data to be translated, captured or in some cases recreated after the fact. Too many times, Gas Company X hires multiple consultants with different disciplines responsible for different project scope items (i.e. Environmental, Right-of-way, Engineering, etc...). Each company has established methods for preparing and organizing their respective data without ever thinking how Gas Company X intends on using the data for other enterprise needs during the project and after the project has been completed. This presentation outlines methods by which companies can require that their project consultants produce project data with geospatial integration in mind. This includes identification of required resources & workflows to specify and manage the data that is prepared and/or collected in a structured environment that is geospatially & data aware.
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Wu, Bian, Xincai Wu, and Jian Huang. "Geospatial data services within Cloud computing environment." In 2010 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2010. http://dx.doi.org/10.1109/icalip.2010.5684381.

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Zhu, Jun, Jianhua Gong, and Hua Qi. "An intelligent data collaboration model in collaborative virtual geographic environment." In Geoinformatics 2006: Geospatial Information Technology, edited by Huayi Wu and Qing Zhu. SPIE, 2006. http://dx.doi.org/10.1117/12.712606.

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Zhu, Qing, Zheng Zhong, and Zhiqiang Du. "Agent model and its applications in 3D virtual design environment." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650376.

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Lei, Liangyu, and Xiaojun Zhou. "Support vector machine based 3D object recognition in a virtual environment." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650273.

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Tang, Lu-liang, and Qing-quan Li. "Study on the grid-based distributed virtual geo-environment (DVGE-G)." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650364.

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Zhu, Jun, Jianhua Gong, Hui Lin, Wenhang Li, Jianqin Zhang, and Xian Wu. "Spatial analysis services in virtual geographic environment based on grid technologies." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650377.

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Lu, Ning, Zhi Zhang, and Yang Gao. "Hyperspectral data recognition and mapping of soil salinization in arid environment." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.651556.

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Zhao, Wenji, Songmei Zhang, and Huili Gong. "Study of Chaobai River wetland resources and environment based on 3S technology." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.651860.

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Reports on the topic "Geospatial data Environment"

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Fischer, Robert, Jeffrey Ruby, Aaron Armstrong, Jarrod Edwards, Nicholas Spore, and Katherine Brodie. Geospatial accuracy of small unmanned airborne system data in the coastal environment. Engineer Research and Development Center (U.S.), March 2019. http://dx.doi.org/10.21079/11681/32302.

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Wright, E. Federal environmental scan of geospatial building data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/314732.

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Best practices for sharing sensitive environmental geospatial data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2010. http://dx.doi.org/10.4095/288863.

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