GIS 정보를 이용한 향상된 모바일 GPS 측위 기법

Geographic Information Systems (GIS) services in academic libraries tend to differ, based on availability of GIS data, software, hardware, and staff expertise. GIS services at the University of Kansas are closely aligned with support for government information, data, maps, and statistics. Thus, our responses to users' needs are often naturally collaborative, optimizing the expertise of multiple staff members and various types of resources. The GIS and Data Specialist assists campus researchers with spatial data and software, as well as facilitating access to GIS data. Lab space for research and coursework involving spatial data is a core component of GIS services. In addition, various levels and types of GIS workshops are offered each semester, and custom training sessions are also available. "Word of mouth" and hands-on workshops are some of the most effective methods of outreach.

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E & P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

The development of geographic information systems (GIS) is now being accompanied by an increasing popularity of Internet GIS mapping services. This study concerns two important issues with Internet GIS interoperability and Open source resources. Centering on the interplay of these two issues, we provide a systematic overview of techniques that have been used to deliver Internet GIS mapping and the relevance of the two issues with regard to these techniques. We then present a standard-and-open-source-based framework for Internet GIS mapping services to achieve data interoperability and high visual quality. The framework employs the Web Feature Service (WFS) to handle vector data and raster data. The Geography Markup Language (GML) standard is used for interoperable data storage and management. The Scalable Vector Graphic (SVG) standard is used to visualize GML data in web browsers. Two preliminary prototypes have been developed to demonstrate the capabilities of the framework. We conclude that using standard-based solutions is an important and viable direction of interoperable Internet GIS services. Advances in standardization and technology have significant implications for Internet GIS mapping. There are many powerful open sources tools which, when coupled with open standards, may be used to develop high-quality online GIS services. Importantly, these solutions require no investment in software, yet achieve excellent interoperability and visualization quality; they are excellent alternative approaches to Internet mapping.

City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R2= 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R2= 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas.

Geographic Information Systems (GIS) data are crucial for many applications such as disaster response, damage assessment, and recovery efforts. The 2010 Haiti earthquake destroyed buildings where GIS data were stored and many of the relief organizations ultimately depended on data retrieved from outside the country to do their work. The lesson learnt from this experience is that open access data is necessary in assisting tasks in such situations and therefore the evaluation of such data for its quality is important. This research evaluates OpenStreetMap (OSM) data that can be and is used by various organizations. The methodology followed is based on the International Organization for Standardization (ISO) Geographic Information-Quality Principles (ISO19113). According to the ISO19113 standard, the data quality elements are: completeness, logical consistency, positional accuracy, thematic accuracy, and temporal accuracy. The first four elements noted above were tested in this study. OSM data was compared with official data from Abu Dhabi city. Results show that the positional accuracy of OSM data meets the American Society for Photogrammetry and Remote Sensing (ASPRS) standards for 1:20,000-scale maps. This indicates that OSM data could be used for many applications, taking into account the hurdles in GIS data acquisition, cost, availability, licensing, distribution policies, and documentation. The article concludes with a call for incorporation of geospatial data services and data quality initiatives in libraries.

Geographic Information System (GIS) is a method of expressing objects in a space. Currently, many research and developments are being conducted to implement 3D GIS. In previous studies, 3D GIS applications have been developed using Unity 3D, which is a 3D engine with good development accessibility. However, it requires manual work to enter various formats of GIS data, making it difficult to immediately reflect GIS data that change frequently. To improve this problem, this study developed a method for automatically reading and outputting various GIS data from the existing Unity 3D application. The improved application could read Satellite Images, Aerial Photographs, Digital Elevation Models (DEM) and Shapefiles with no transformation through other commercial programs, and they could be implemented as 3D objects. This study automated the GIS data conversion which had been manually performed and as a result, the manpower, time, and resources required for 3D GIS implementation can be saved.

This dataset provides a high-resolution spatial classification of urban morphology on Reunion Island, France, using a 100 m x 100 m grid based on the Local Climate Zone (LCZ) framework. The data were generated through a combination of satellite imagery analysis using the World Urban Database and Access Portal Tools (WUDAPT) method and Geographic Information System (GIS) data integration. The classification was refined using building height and surface fraction data from the BD TOPO 2018 database, improving the accuracy of urban form representation.The dataset is provided in shapefile format, facilitating its use in GIS software by urban planners, researchers, and policymakers. Each grid point contains geospatial and morphological attributes, including LCZ classification, average building height (m), and built surface fraction (%). These parameters allow researchers to spatially correlate urban morphology with observed temperature data, supporting targeted urban heat island (UHI) studies. The most compact LCZ types (LCZ 1, 2, and 3) are mapped in detail, highlighting zones on the island most exposed to climatic risks. These high-risk areas should be identified and prioritized for climate mitigation strategies.Beyond UHI analysis, the dataset allows for the identification of land use patterns and urban growth trends by analyzing the spatial distribution of LCZ across the island. Areas of recent expansion, often characterized by transitions from natural zones (LCZ 11, 14) to LCZ 6 or 9 (low-rise and sparsely built, can be geolocated and compared with zoning regulations. This offers urban planners a tool to evaluate whether current development aligns with local urban containment policies and to identify zones at risk of unregulated sprawl.Because the LCZ classification system is internationally standardized, the dataset also supports comparative urban climate studies. It can be directly used to contrast Reunion’s urban structure with other small island developing states, such as Mauritius, in order to evaluate similarities and differences in urbanization dynamics under similar climatic constraints.

Agricultural fields, grasslands, and forests are very important areas for groundwater recharge. However, these types of land cover in the Kumamoto area, Japan, were damaged by the Kumamoto earthquake and heavy rains in 2016. In this region, where groundwater provides almost 100% of the domestic water supply for a population of about 1 million, quantitative evaluation of changes in groundwater recharge due to land cover changes induced by natural disasters is important for the sustainable use of groundwater in the future. The objective of this study was to create a land cover map and estimate the groundwater recharge in 2016. Geographic information system (GIS) data and SPOT 6/7 satellite images were used to classify the Kumamoto area into nine categories. The maximum likelihood classifier of supervised classification was applied in ENVI 5.6. Eventually, the map was cleaned up with a 21 × 21 kernel filter, which is larger than the common size of 3 × 3. The created land cover map showed good performance of the larger filter size and sufficient validity, with overall accuracy of 91.7% and a kappa coefficient of 0.88. The estimated total groundwater recharge amount reached 757.56 million m3. However, if areas of paddy field, grassland, and forest had not been reduced due to the natural disasters, it is estimated that the total groundwater recharge amount would have been 759.86 million m3, meaning a decrease of 2.30 million m3 in total. The decrease of 2.13 million m3 in the paddy fields is temporary, because the paddy fields and irrigation channels have been improved and the recharge amount will recover. On the other hand, since the topsoil on the landslide scars will not recover easily in natural conditions, it is expected to take at least 100 years for the groundwater recharge to return to its original state. The recharge amount was estimated to decrease by 0.17 million m3 due to landslides. This amount is quite small compared to the total recharge amount. However, since the reduced recharge amount accounts for the annual water consumption for 1362 people, and 12.1% of the recharge decrease of 1.41 million m3 each year to fiscal year 2024 is expected by municipalities, we conclude that efforts should be made to compensate for the reduced amount due to the disasters.

Modelling landscape distributions of large forest owls as applied to managing forests in north-east Victoria, Australia

This paper proposes a novel vision-aided navigation architecture to aid the inertial navigation system (INS) for accurate unmanned aerial vehicle (UAV) localization. Unlike previous image localization methods such as scene matching and terrain contour matching, our approach registers meaningful object-level features extracted from real-time aerial imagery with the data of geographic information system (GIS). Firstly, we extract from aerial images the widely distributed object features including roads, rivers, road intersections, villages, bridges et al.. Then, the extracted image features are delineated as geometrical points and vectors, which coincide with the representation of GIS data. Finally, GIS model is constructed by corresponding geographical object information from GIS data, and visual geometrical features are registered with GIS model to obtain the absolute position of the image. The proposed method adopts GIS as reference data, thus the storage requirement is lower than that of scene matching. In addition, all steps of this approach can be calculated efficiently, while the computational cost of terrain contour matching is very high. Simulation results demonstrate the feasibility of the proposed method for UAV localization.

The Minnesota Lake Superior shoreline (Minnesota'S “North Shore”) is characterized by a rocky shoreline with stretches of sand, gravel and cobble as well as several stream and ditch inlets into Lake Superior. The risk of spills is from both land and water. The 150 plus mile stretch of shoreline has a very limited amount of response equipment or nearby responders. The Duluth-Superior Harbor is at the estuarine mouth of the St. Louis River and is divided by the Minnesota and Wisconsin border. The harbor includes sensitive shorelines and habitats, numerous stream, ditch and storm sewer outfalls, as well as an active shipping industry. Geographic Information System (GIS) is a great tool for conducting data analysis and planning. GIS allows users to analyze and visualize large volumes of data to make decisions quickly. While many spill response planners use GIS as a planning response tool, many responders do not have the training and equipment to make efficient use of the capabilities of GIS. This project uses GIS to gather and analyze available data and then put it into a useful “hardcopy” visual format for responders to use in the field. Maps produced from various GIS data sources outline the response strategies for sensitive areas to be protected; types and quantities of response equipment needed; access areas; and other important features. These “hardcopy” displays can be included in contingency plans. Larger laminated versions can be stored with response equipment caches. For response planners that are more familiar with GIS, the tool includes several useful features including “clickable” hyperlinks that connect to georeferenced oblique aerial photos of the Lake Superior shoreline. Also included are GIS data from U.S. EPA and NOAA, including sensitive environment, economic and cultural resources; potential spill sources; and response resources. Where available, sewer data for both sanitary and storm sewers is included, along with high resolution aerial photos; navigation charts; highway stream crossing data; and other habitat information. These various data sources are valuable tools for spill response planners to more accurately and effectively develop workable response strategies and also for use during an actual response to more effectively and efficiently direct responders and resources. This GIS product also incorporates an Access database which allows for easy report generation and information updating.

This report identifies effective and emerging data exchange practices that airports and public agencies can use to increase the geographic information system (GIS) data they have access to, while reducing the cost of identifying, collecting, and maintaining these data. It synthesizes the need expressed by airports and public agencies to exchange geographic information and related resources, highlighting effective practices and industry trends. Nine case examples are summarized. The report findings are based on a literature search, as well as information from organizations that responded to the on-line survey and/or agreed to be interviewed by phone. The audience for this report is airport planners, designers, engineers, and GIS specialists and their counterparts in public agencies.

The basic properties to be dealt with, when considering initially the research needs related to the integration of remote sensing (RS) information into a geographic information system (GIS), are many-sided. The primary combination of remote sensing and GIS is mainly realized by the transforms of data structure. Because of its own limitations, there is an urgent need to investigate the integration of RS and GIS in higher levels. In this paper, we discuss the different types of combinations of RS with GIS, and propose that GIS data should be directly brought into image processing from the beginning. A tentative idea of how to use the method of granularity to study the common processing unit of RS and GIS is described. The example for the determination of granularity of spatial data processing related to run-length-code line is also given.

Map labels provide valuable geographic information by annotating geographic phenomenona with text descriptions. However, many interesting and useful maps are only available as images and hence this information is not readily accessible in a Geographic Information System (GIS). Previous work on text recognition in maps considers maps as a special type of image to be processed using Optical Character Recognition (OCR) techniques and does not pay attention to the typical workflows in a GIS. As a result, to convert map labels into machine-readable text, a user has to switch between OCR and GIS software, transform the detected text locations from the image coordinates (in OCR) to the map coordinates (in GIS), and apply data import/export procedures. This tedious process limits the opportunity to access text information in maps. This paper presents ArcStrabo, an integration of our previous text recognition work and a GIS, which uses a GIS user interface, workflows, and data types to enable efficient training of text recognition algorithms for converting map labels to a table of geographic names. We show that ArcStrabo facilitates map digitization processes, eliminates the need for GIS users to learn additional OCR tools, and does not require manual data export/import procedures between GIS and OCR software.

Chapter 13 - Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture