범용 3D 그래픽 엔진의 GIS 정보 서비스를 위한 데이터 자동변환 알고리즘 개발

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.

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models.

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.

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.

This paper introduces a novel approach for mapping process description with Scan data to Building Information Modeling (BIM) in a 3D Geographic Information System (GIS). The methodology focuses on automatically generating building mass and facade information on the GIS platform using Point Cloud Data (PCD) of Airborne Laser Scanning (ALS). Advanced scanning techniques capture detailed geometry from the physical site and generate high-resolution point clouds, which are processed to create 3D models for GIS integration. The critical contribution of this research lies in a scalable Scan to BIM mapping process, which can be used for generating building footprints and masses, including attributes, on 3D GIS. The resulting integrated BIM–GIS dataset provides an accurate building mass, facade information, facility asset management, and architectural design and facilitates improved decision-making in urban planning based on enhanced visualization, analysis, and simulation. This study suggests a flexible Scan to BIM mapping process description based on use cases, including algorisms. Through prototype development, a case study demonstrates the effectiveness of the process approach, the automatic generation of BIM on a 3D GIS platform, and reducing the manual efforts. The proposed method automatically creates DEM, SHP, GeoJSON, IFC, and coordinate system information from scan data and can effectively map building objects in 3D GIS.

A Study on the Construction of the Unity 3D Engine Based on the WebGIS System for the Hydrological and Water Hazard Information Display

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

Integrating multiple data sources is a very important strategy to obtain relevant solutions in geo‐scientific analysis. This paper mainly deals with the integration of Geographical Information System (GIS) data, stereo aerial imagery and a Digital Surface Model (DSM) to extract wind erosion obstacles (namely tree rows and hedges) in open landscapes. Different approaches, such as image segmentation, edge extraction, linking, grouping and 3‐dimensional verification with the DSM, are combined to extract the objects of interest. Experiments show that most wind erosion obstacles can be successfully extracted by the developed system. Most of this work was carried out while the corresponding author was with the Institute of Photogrammetry and GeoInformation, University of Hannover, Germany.

While the use of three-dimensional (3D) geographical information system (GIS) is becoming in rapid development and being used in various fields such as urban and regional planning, disaster management and planning, mobile navigation and etc., commercial and open source GIS software packages tend to offer 3D-GIS functionalities for their products. On the basis, GIS analysis functions are to provide information with respect to geographical location and by having 3D spatial data as an input, it will give advantages in providing horizontal position information. However, to analyze moving objects (temporal) in 3D seems not an easy task and not fully supported by current GIS platform packages. Previously in two-dimensional (2D) GIS practice, main issue addressed by researchers in managing temporal spatial objects is GIS packages were designed based on hardware and software constraints whereby it should be based on the temporal spatial objects ontology. Nowadays, the trend of managing temporal 3D data is via 3D spatial simulation or animation. This approach will not in assistance for GIS users in conducting spatial queries. Without having a suitable ontology and valid topological data structure for temporal 3D data, it will cause repetitive of temporal data (redundancy) and complications in executing spatial analysis in 3D environment. Therefore this paper focuses on the ontology for managing moving 3D spatial objects (i.e. air pollution, flood). The characteristics of moving objects were reviewed thoroughly by categorizing it based on its different appearances. Moreover, existing methods in managing temporal database were addressed and discussed for its practicalities. Another important aspect in managing temporal 3D objects is the implementation of topological data structures for 3D spatial objects were reviewed. In the last section of this paper it summarized the issues and further ideas towards implementing and managing temporal 3D spatial objects in GIS based on the Geoinformation Ontology (GeO).

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

Background: Cycling as a means of transport contributes to public health in terms of increasing the physical activity behaviour and reducing air pollution. In comparison to walking, cycling-spe cific environmental characteristics have been less investigated. The aim of this study was to develop, based on geographic information systems (GIS) data, a bikeability index for a mid-sized European city, to examine the predictive validity of the index and to visualize the bicycle-friendliness of the city by creating a bikeability map. Methods: Using GIS data, we assessed the environmental characteristics of 278 bicycle trips in the city of Graz, Austria. Characteristics which differed significantly between the actually taken and shortest possible routes were used to form an additive bikeability index and the bikeability map for 100 m × 100 m cells. The relationship between the cycling behaviour and the bikeability index around the home environment was examined to assess the predictive validity of the index. Results: Three environmental components (cycling infrastructure, bicycle pathways and green areas) were positively related, and two components (main roads, and topography) were negatively related to the actually used route. These components formed the bikeability index, which was positively correlated with cycling behaviour (OR: 1.08, 95% CI 1.01 - 1.17). The final outcome is a high-resolution coloured map indicating the degree of bicycle-friendliness in the city of Graz. Conclusion: Mapping based on the bikeability index helps to visualize the bicycle-friendliness of an urban area. Therefore, it should be a useful tool for the planning as well as for the evaluation of the transport environments in cities.

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.