VECTOR AND RASTER DATA LAYERED FUSION AND 3D VISUALIZATION

Abstract. Although contemporary geospatial science has made great progress, spatial data fusion of vector and raster data is still a problem in the geoinformation science environment. In order to solve the problem, this paper proposes a method which merges vector and raster data. Firstly, the row and column numbers of the raster data, and the X, Y values of the vector data are represented by Morton code in the C++ environment, respectively. Secondly, we establish the the raster data table and the vector data table in the Oracle database to store the vector data and the raster data. Third, this paper uses the minimum selection bounding box method to extract the top data of the building model. Finally, we divide the vector and raster data into four steps to obtain the fusion data table, and we call the fusion data in the database for 3D visualization. This method compresses the size of data of the original data, and simultaneously divides the data into three levels, which not only solves the problem of data duplication storage and unorganized storage, but also can realize vector data storage and the raster data storage in the same database at the same time. Thus, the fusion original orthophoto data contains the gray values of building roofs and the elevation data, which can improve the availability of vector data and the raster data in the 3D Visualization application.

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VECTOR AND RASTER DATA STORAGE BASED ON MORTON CODE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-2523-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 2523-2526

Author(s):

G. Zhou ◽

Q. Pan ◽

T. Yue ◽

Q. Wang ◽

H. Sha ◽

...

Keyword(s):

Data Storage ◽

Spatial Data ◽

Image Data ◽

Geographic Information ◽

Storage Space ◽

Vector Data ◽

Raster Data ◽

Oracle Database ◽

Position Information ◽

University Of Technology

Even though geomatique is so developed nowadays, the integration of spatial data in vector and raster formats is still a very tricky problem in geographic information system environment. And there is still not a proper way to solve the problem. This article proposes a method to interpret vector data and raster data. In this paper, we saved the image data and building vector data of Guilin University of Technology to Oracle database. Then we use ADO interface to connect database to Visual C++ and convert row and column numbers of raster data and X Y of vector data to Morton code in Visual C++ environment. This method stores vector and raster data to Oracle Database and uses Morton code instead of row and column and X Y to mark the position information of vector and raster data. Using Morton code to mark geographic information enables storage of data make full use of storage space, simultaneous analysis of vector and raster data more efficient and visualization of vector and raster more intuitive. This method is very helpful for some situations that need to analyse or display vector data and raster data at the same time.

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CitySAC: A Query-Able CityGML Compression System

Smart Cities ◽

10.3390/smartcities2010008 ◽

2019 ◽

Vol 2 (1) ◽

pp. 106-117

Author(s):

Chengxi Siew ◽

Pankaj Kumar

Keyword(s):

Data Storage ◽

Spatial Data ◽

City Planning ◽

Data Transfer ◽

Original Data ◽

File Size ◽

Compression Technique ◽

3D Data ◽

Data Infrastructures ◽

Data Efficiency

Spatial Data Infrastructures (SDIs) are frequently used to exchange 2D & 3D data, in areas such as city planning, disaster management, urban navigation and many more. City Geography Mark-up Language (CityGML), an Open Geospatial Consortium (OGC) standard has been developed for the storage and exchange of 3D city models. Due to its encoding in XML based format, the data transfer efficiency is reduced which leads to data storage issues. The use of CityGML for analysis purposes is limited due to its inefficiency in terms of file size and bandwidth consumption. This paper introduces XML based compression technique and elaborates how data efficiency can be achieved with the use of schema-aware encoder. We particularly present CityGML Schema Aware Compressor (CitySAC), which is a compression approach for CityGML data transaction within SDI framework. Our test results show that the encoding system produces smaller file size in comparison with existing state-of-the-art compression methods. The encoding process significantly reduces the file size up to 7–10% of the original data.

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Techniques and Methods of Spatial Data Fusion

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.3274 ◽

2012 ◽

Vol 263-266 ◽

pp. 3274-3278

Author(s):

Hui Ming Yu ◽

Jian Zhong Guo ◽

Yi Cheng ◽

Qian Lou

Keyword(s):

Data Fusion ◽

Information Fusion ◽

Spatial Data ◽

Data Model ◽

Semantic Information ◽

Utilization Efficiency ◽

Vector Data ◽

Raster Data ◽

Information Precision ◽

Spatial Data Integration

Spatial data fusion is an important method of spatial data acquisition. The aim of multisource spatial data integration and fusion is to improve the information precision and information's utilization efficiency. Vector and raster are the two main spatial data structures. This article discusses vector data fusion from of data model fusion, semantic information fusion and coordinates unification, reviews the main methods of raster data fusion and discusses the key technologies of vector and raster data fusion, and proposes the future developments of spatial data fusion technique.

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A spatial data generalization method for online vector data 3D visualization

10.1117/12.759688 ◽

2007 ◽

Author(s):

Min Sun ◽

Zhipeng Wang ◽

Dan Yin ◽

Huan Wu

Keyword(s):

Spatial Data ◽

3D Visualization ◽

Vector Data ◽

Data Generalization

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Literature Mapper: A QGIS Plugin for Georeferencing Citations in Zotero

Air Soil and Water Research ◽

10.1177/11786221211009209 ◽

2021 ◽

Vol 14 ◽

pp. 117862212110092

Author(s):

Michele M Tobias ◽

Alex I Mandel

Keyword(s):

Data Storage ◽

Spatial Data ◽

Future Research ◽

Web Mapping ◽

Web Map ◽

Public Sharing ◽

Academic Publications ◽

Spatial Coordinates ◽

Study Sites ◽

Soil And Water

Many studies in air, soil, and water research involve observations and sampling of a specific location. Knowing where studies have been previously undertaken can be a valuable addition to future research, including understanding the geographical context of previously published literature and selecting future study sites. Here, we introduce Literature Mapper, a Python QGIS plugin that provides a method for creating a spatial bibliography manager as well as a specification for storing spatial data in a bibliography manager. Literature Mapper uses QGIS’ spatial capabilities to allow users to digitize and add location information to a Zotero library, a free and open-source bibliography manager on basemaps or other geographic data of the user’s choice. Literature Mapper enhances the citations in a user’s online Zotero database with geo-locations by storing spatial coordinates as part of traditional citation entries. Literature Mapper receives data from and sends data to the user’s online database via Zotero’s web API. Using Zotero as the backend data storage, Literature Mapper benefits from all of its features including shared citation Collections, public sharing, and an open web API usable by additional applications, such as web mapping libraries. To evaluate Literature Mapper’s ability to provide insights into the spatial distribution of published literature, we provide a case study using the tool to map the study sites described in academic publications related to the biogeomorphology of California’s coastal strand vegetation, a line of research in which air movement, soil, and water are all driving factors. The results of this exercise are presented in static and web map form. The source code for Literature Mapper is available in the corresponding author’s GitHub repository: https://github.com/MicheleTobias/LiteratureMapper

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Olomouc - Possibilities of Geovisualization of the Historical City

Geoinformatics FCE CTU ◽

10.14311/gi.6.33 ◽

2011 ◽

Vol 6 ◽

pp. 267-274

Author(s):

Stanislav Popelka ◽

Alžběta Brychtová

Keyword(s):

Spatial Data ◽

Large Scale ◽

18Th Century ◽

3D Visualization ◽

3D Models ◽

Google Earth ◽

Point Of View ◽

World Heritage Site ◽

Historical Maps ◽

The City

Olomouc, nowadays a city with 100,000 inhabitants, has always been considered as one of the most prominent Czech cities. It is a social and economical centre, which history started just about the 11th century. The present appearance of the city has its roots in the 18th century, when the city was almost razed to the ground after the Thirty years’ war and a great fire in 1709. After that, the city was rebuilt to a baroque military fortress against Prussia army. At the beginning of the 20th century the majority of the fortress was demolished. Character of the town is dominated by the large number of churches, burgher’s houses and other architecturally significant buildings, like a Holy Trinity Column, a UNESCO World Heritage Site. Aim of this project was to state the most suitable methods of visualization of spatial-temporal change in historical build-up area from the tourist’s point of view, and to design and evaluate possibilities of spatial data acquisition. There are many methods of 2D and 3D visualization which are suitable for depiction of historical and contemporary situation. In the article four approaches are discussed comparison of historical and recent pictures or photos, overlaying historical maps over the orthophoto, enhanced visualization of historical map in large scale using the third dimension and photorealistic 3D models of the same area in different ages. All mentioned methods were geolocalizated using the Google Earth environment and multimedia features were added to enhance the impression of perception. Possibilities of visualization, which were outlined above, were realized on a case study of the Olomouc city. As a source of historical data were used rapport plans of the bastion fortress from the 17th century. The accuracy of historical maps was confirmed by cartometric methods with use of the MapAnalyst software. Registration of the spatial-temporal changes information has a great potential in urban planning or realization of reconstruction and particularly in the propagation of the region and increasing the knowledge of citizens about the history of Olomouc.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b4-385-2016 ◽

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.

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The Design and Testing of 3DmoveR: an Experimental Tool for Usability Studies of Interactive 3D Maps

Cartographic Perspectives ◽

10.14714/cp90.1411 ◽

2018 ◽

pp. 31-63 ◽

Cited By ~ 3

Author(s):

Lukáš Herman ◽

Tomáš Řezník ◽

Zdeněk Stachoň ◽

Jan Russnák

Keyword(s):

Spatial Data ◽

Response Times ◽

3D Visualization ◽

User Interaction ◽

Google Earth ◽

Web Based ◽

3D Environments ◽

Camera Position ◽

Interactive 3D ◽

3D Scene

Various widely available applications such as Google Earth have made interactive 3D visualizations of spatial data popular. While several studies have focused on how users perform when interacting with these with 3D visualizations, it has not been common to record their virtual movements in 3D environments or interactions with 3D maps. We therefore created and tested a new web-based research tool: a 3D Movement and Interaction Recorder (3DmoveR). Its design incorporates findings from the latest 3D visualization research, and is built upon an iterative requirements analysis. It is implemented using open web technologies such as PHP, JavaScript, and the X3DOM library. The main goal of the tool is to record camera position and orientation during a user’s movement within a virtual 3D scene, together with other aspects of their interaction. After building the tool, we performed an experiment to demonstrate its capabilities. This experiment revealed differences between laypersons and experts (cartographers) when working with interactive 3D maps. For example, experts achieved higher numbers of correct answers in some tasks, had shorter response times, followed shorter virtual trajectories, and moved through the environment more smoothly. Interaction-based clustering as well as other ways of visualizing and qualitatively analyzing user interaction were explored.

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