Journal articles on the topic 'Geospatial information systems and geospatial data modelling'

Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis.

<p><strong>Abstract.</strong> The use of intelligent technologies within 3D geospatial data analysis and management will decidedly open the door towards efficiency, cost transparency, and on-time schedules in planning processes. Furthermore, the mission of smart cities as a future option of urban development can lead to an environment that provides high-quality life along stable structures. However, neither geospatial information systems nor building information modelling systems seem to be well prepared for this new development. After a review of current approaches and a discussion of their limitations we present our approach on the way to an intelligent platform for the management and analysis of big 3D geospatial data focusing on infrastructure projects such as metro or railway tracks planning. three challenges are presented focusing on the management of big geospatial data with existing geo-database management systems, the integration of heterogeneous data, and the 3D visualization for database query formulation and query results. The approach for the development of a platform for big geospatial data analysis is discussed. Finally, we give an outlook on our future research supporting intelligent 3D city applications in the United Arab Emirates.</p>

This study aims to improve the implementation of models of geospatial information in Web Ontology Language (OWL). Large amounts of geospatial information are maintained in Geographic Information Systems (GIS) based on models according to the Unified Modeling Language (UML) and standards from ISO/TC 211 and the Open Geospatial Consortium (OGC). Sharing models and geospatial information in the Semantic Web will increase the usability and value of models and information, as well as enable linking with spatial and non-spatial information from other domains. Methods for conversion from UML to OWL for basic concepts used in models of geospatial information have been studied and evaluated. Primary conversion challenges have been identified with specific attention to whether adapted rules for UML modelling could contribute to improved conversions. Results indicated that restrictions related to abstract classes, unions, compositions and code lists in UML are challenging in the Open World Assumption (OWA) on which OWL is based. Two conversion challenges are addressed by adding more semantics to UML models: global properties and reuse of external concepts. The proposed solution is formalized in a UML profile supported by rules and recommendations and demonstrated with a UML model based on the Intelligent Transport Systems (ITS) standard ISO 14825 Geographic Data Files (GDF). The scope of the resulting ontology will determine to what degree the restrictions shall be maintained in OWL, and different conversion methods are needed for different scopes.

Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

Geographic information systems (GIS) have been used for geospatial data management and analysis map production and spatial modelling They also have the potential to incorporate climate and satellite data that could provide a spatial perspective on risk of establishment of plant pathogens In the present case study the climatic suitability for establishment of dwarf bunt a disease that can cause market access restrictions on wheat or other grass hosts in New Zealand was analysed using a GIS approach Establishment risk for dwarf bunt in New Zealand was found to be very low GIS was found to be a more versatile tool for modelling potential geographic distribution of organisms than conventional climate matching tools such as CLIMEX

This study aims to improve interoperability between Geographic Information Systems (GIS) and geospatial databases for Intelligent Transport Systems (ITS). Road authorities maintain authoritative information for legal and safe navigation in GIS databases. This information needs to be shared with ITS databases for route planning and navigation, and for use in combination with local knowledge from vehicle sensors. Current solutions for modelling and exchanging geospatial information in the domains of GIS and ITS have been studied and evaluated. Limitations have been pointed out related to usability in the GIS domain and flexibility for representing an evolving real world. A prototype for an improved information exchange model has been developed, based on ISO/TC 211 standards, Model Driven Architecture (MDA), and concepts from the studied solutions. The prototype contains generic models for feature catalogues and features, with implementation schemas in the Geography Markup Language (GML). Results from a case study indicated that the models could be implemented with feature catalogues from the ITS standard ISO 14825 Geographic Data Files (GDF) and the INSPIRE Transport Networks specification. The prototype can be a candidate solution for improved information exchange from GIS databases to ITS databases that are based on the Navigation Data Standard.

Abstract. More and more cities declare themselves to be a smart city or plan to be the same. Smart cities require a solid data source as basis for all further actions and the urban digital twin is the basis on which all information is collected and analysed. The urban digital twin is much more than just a 3D city model, but often this together with GIS data is the starting point for the urban digital twin. The basis of the urban digital twin is formed by geospatial data in the form of the geospatial digital twin. The digital twin hereby acts as a kind of hub into which all relevant and available information is included and analysed. To generate a geospatial digital twin aerial sensors that collect multiple data simultaneously, hybrid sensors, are perfectly suited for this task. In aerial data acquisition a new era started with the introduction of the first real hybrid sensor systems, like the Leica CityMapper-2. Hybrid in this context means the combination of an (oblique) camera system with a topographic LiDAR into an integrated aerial mapping system. By combining these complimentary sub-systems into one system the weaknesses of the one system could be compensated by using the alternative data source. An example is the mapping of low-light urban canyons, where image-based systems mostly produce unreliable results. For an LiDAR sensor the geometrical reconstruction of these areas is straight forward and leads to accurate results. The paper gives a detailed overview over the development and technical characteristics of hybrid sensor systems. The process of data acquisition is discussed and strategies for hybrid urban mapping are proposed. Furthermore, the paper provides insights into the advantage of LiDAR data for the 3D Mesh generation for urban modelling and on the possibilities to generate new products from the combination of the single products with the help of GeoAI. Finally, the use and some use cases of the hybrid sensor data and the derived products in the context of the urban digital twin is discussed and with the infinite loop of data, analysis, and action it is shown, that all data from the urban digital twin can only be a snapshot at a given point in time and the data recording and analysis is a permanent loop.

In recent years, 3D virtual indoor/outdoor urban modelling becomes a key spatial information framework for many civil and engineering applications such as evacuation planning, emergency and facility management. For accomplishing such sophisticate decision tasks, there is a large demands for building multi-scale and multi-sourced 3D urban models. Currently, Building Information Model (BIM) and Geographical Information Systems (GIS) are broadly used as the modelling sources. However, data sharing and exchanging information between two modelling domains is still a huge challenge; while the syntactic or semantic approaches do not fully provide exchanging of rich semantic and geometric information of BIM into GIS or vice-versa. This paper proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graphs. The novelty of the proposed solution comes from the benefits of integrating BIM and GIS technologies into one unified model, so-called Integrated Geospatial Information Model (IGIM). The proposed approach consists of three main modules: BIM-RDF and GIS-RDF graphs construction, integrating of two RDF graphs, and query of information through IGIM-RDF graph using SPARQL. The IGIM generates queries from both the BIM and GIS RDF graphs resulting a semantically integrated model with entities representing both BIM classes and GIS feature objects with respect to the target-client application. The linkage between BIM-RDF and GIS-RDF is achieved through SPARQL endpoints and defined by a query using set of datasets and entity classes with complementary properties, relationships and geometries. To validate the proposed approach and its performance, a case study was also tested using IGIM system design.

In recent years, 3D virtual indoor/outdoor urban modelling becomes a key spatial information framework for many civil and engineering applications such as evacuation planning, emergency and facility management. For accomplishing such sophisticate decision tasks, there is a large demands for building multi-scale and multi-sourced 3D urban models. Currently, Building Information Model (BIM) and Geographical Information Systems (GIS) are broadly used as the modelling sources. However, data sharing and exchanging information between two modelling domains is still a huge challenge; while the syntactic or semantic approaches do not fully provide exchanging of rich semantic and geometric information of BIM into GIS or vice-versa. This paper proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graphs. The novelty of the proposed solution comes from the benefits of integrating BIM and GIS technologies into one unified model, so-called Integrated Geospatial Information Model (IGIM). The proposed approach consists of three main modules: BIM-RDF and GIS-RDF graphs construction, integrating of two RDF graphs, and query of information through IGIM-RDF graph using SPARQL. The IGIM generates queries from both the BIM and GIS RDF graphs resulting a semantically integrated model with entities representing both BIM classes and GIS feature objects with respect to the target-client application. The linkage between BIM-RDF and GIS-RDF is achieved through SPARQL endpoints and defined by a query using set of datasets and entity classes with complementary properties, relationships and geometries. To validate the proposed approach and its performance, a case study was also tested using IGIM system design.

Abstract. This paper presents a system architecture for structuring and manipulation of Building Information Models (BIM), three-dimensional (3D) geospatial information, point clouds and time series data obtained from sensors. The system consists of four layers including data pre-processing, data structuring and storage, system interface and front-end data manipulation. To enable the integration of different data, a unified UML model is developed. The paper explains all steps of 3D reconstruction, BIM geo-referencing, storage of spatial data and visualisation. Special attention is given to the integration of sensors data. The data model and the system architecture are tested for a university campus. The results demonstrate an approach for BIM-GIS-Sensor integration as part of Precinct Information Modelling (PIM). The system architecture allows for a flexible structuring and manipulation of different spatial data towards managing various 3D spatial and non-spatial data.

The subject matter of the article is thematic interpretation of species images. The goal of the work is to develop a model for thematic interpretation of species images, which will be based on the model for the formation of species images and additionally take into account geographic zoning. The following task was solved in the article: development of a model of thematic interpretation of view images, which takes into account the shortcomings of existing models and is based on the model of formation of view images, carries out the reverse transformation of the image coordinates into geospatial coordinates and clustering the image into separate classes according to their color and texture, additionally taking into account geographic zoning, which provides the possibility of advanced analysis and forecasting the temporal dynamics of data in geospatial information processing systems. The following methods used are – mathematical apparatus of the theory of matrices, methods of mathematical modelling, methods of data clustering, methods of differential calculus, methods of digital image processing. The following results were obtained – groups of existing models for interpreting the results of remote sensing of the Earth, their advantages and disadvantages have been analyzed; a mathematical model of the formation of a species image for a section of the earth's surface has been formulated in general form; a mathematical model of thematic interpretation of species images was formulated in general form; the operator of transformation of coordinates, operators of clustering, operators of zoning and their explicit form are considered; a model of thematic interpretation of species images in operator form is obtained. Conclusions: for the first time, a model of thematic interpretation of view images was developed, which, based on the model of formation of view images, carries out the inverse transformation of coordinates into geospatial coordinates and clustering by their color and texture, additionally taking into account geographical zoning, which provides the possibility of advanced analysis and forecasting of the temporal dynamics of data in processing systems geospatial information.

AbstractThe possibilities of disease prediction based on the environmental characteristics of geographical areas and specific requirements of the causative infectious agents are reviewed and, in the case of parasites whose life cycles involve more than one host, the needs of the intermediate hosts are also referred to. The geographical information systems framework includes epidemiological data, visualization (in the form of maps), modelling and exploratory analysis using spatial statistics. Examples include climate-based forecast systems, based on the concept of growing degree days, which now exist for several parasitic helminths such as fasciolosis, schistosomiasis, dirofilariasis and also for malaria. The paper discusses the limits of data collection by remote sensing in terms of resolution capabilities (spatial, temporal and spectral) of sensors on-board satellites. Although the data gained from the observation of oceans, land, elevations, land cover, land use, surface temperatures, rainfall, etc. are primarily for weather forecasting, military and commercial use, some of this information, particularly that from the climate research satellites, is of direct epidemiological utility. Disease surveillance systems and early-warning systems (EWS) are prime examples of academic approaches of practical importance. However, even commercial activities such as the construction of virtual globes, i.e. computer-based models of the Earth, have been used in this respect. Compared to conventional world maps, they do not only show geographical and man-made features, but can also be spatially annotated with data on disease distribution, demography, economy and other measures of particular interest.

The distribution of diseases caused by vector-borne viruses and parasites are restricted by the environmental requirements of their vectors, but also by the ambient temperature inside the host as it influences the speed of maturation of the infectious agent transferred. The launch of the Soil Moisture Active Passive (SMAP) satellite in 2015, and the new ECOSTRESS instrument onboard the International Space Station (ISS) in 2018, established the leadership of the National Aeronautics Space Administration (NASA) in ecology and climate research by allowing the structural and functional classification of ecosystems that govern vector sustainability. These advances, and the availability of sub-meter resolution data from commercial satellites, contribute to seamless mapping and modelling of diseases, not only at continental scales (1 km2) and local community or agricultural field scales (15–30 m2), but for the first time, also at the habitat–household scale (<1 m2). This communication presents current capabilities that are related to data collection by Earth-observing satellites, and draws attention to the usefulness of geographical information systems (GIS) and modelling for the study of important parasitic diseases.

Incorporating data stored in a geographical information system (GIS) within the development of hydraulic simulation models is crucial for operating, updating, and hence redesigning water supply systems (WSS). Building and updating hydraulic models can be both time and resource consuming; moreover, the need to update infrastructure cadastral information makes the model itself outdated. In addition, typical dispersion of data across several databases requires extra effort to maintain the whole system and ensure it is properly assembled. Albeit there are some GIS-based hydraulic modelling solutions available, they typically use external connections to assemble all components resulting in additional costs and less flexibility. In order to be able to establish a single fully integrated data model towards global characterization of a WSS and associate hydraulic simulation, this paper proposes the specific implementation of an EPANET 2 model in PostgreSQL along with PostGIS extension. The system developed enables the construction of the model, hydraulic simulation, and storage of results within a single database. Required procedures and functions were coded either in pgSQL or Python and their execution were carried out using SQL statements. Finally, a case study was selected in order to test the system proposed. Results show that an integrated approach indeed allows the expedited creation of more realistic hydraulic models based on the stored cadastral information.

Geospatial data and information within contemporary land administration systems are fundamental to manage the territory adequately. 3D land administration systems, often addressed as 3D cadastre, promise several benefits, particularly in managing today’s complex built environment, but these are currently still non-existent in their full capacity. The development of any complex information and administration system, such as a land administration system, is time-consuming and costly, particularly during the phase of evaluation and testing. In this regard, the process of implementing such systems may benefit from using synthetic data. In this study, the method for simulating the 3D cadastral dataset is presented and discussed. The dataset is generated using a procedural modelling method, referenced to real cadastral data for the Slovenian territory and stored in a spatial database management system (DBMS) that supports storage of 3D spatial data. Spatial queries, related to 3D cadastral data management, are used to evaluate the database performance and storage characteristics, and 3D visualisation options. The results of the study show that the method is feasible for the simulation of large-scale 3D cadastral datasets. Using the developed spatial queries and their performance analysis, we demonstrate the importance of the simulated dataset for developing efficient 3D cadastral data management processes.

This paper provides an overview of the accuracy standards, specifications, and recent advances of engineering survey operations for the design and construction of railway tunnels in Hong Kong. It covers geodetic control, deformation monitoring surveys, detail mapping, geometric modelling, setting-out, asbuilt surveys and total quality management (TQM). Recent advances include an integrative approach to: (1) combine measurements collected by geodetic and geotechnical systems including interferometric synthetic aperture radar (InSAR) in monitoring ground displacements and structural deformation at local and regional scales; (2) map geospatial data into computer aided design (CAD) formats and geographic information systems (GIS) by both conventional and mobile surveying systems; (3) collect as-built data of railway tracks and tunnels by positioning instruments, track sensors and laser scanners together; (4) incorporate Outsource Plan into the TQM system under ISO 9001:2008.

Abstract. Urban dynamics modelling using system dynamic (SD) approaches aims to provide an understanding of the major internal forces within an urban area, such as population development. SD models provide valuable information for decision and policy making. Urban systems are strongly related to the urban space, which is well described by geospatial data. Therefore, the connection of SD and geospatial data is advantageous, both for feeding spatial information into SD models and for further spatial analyses and for visualizing the results of SD in geographic context. This paper describes a new approach to combine an SD model with a semantic 3D city model. Our approach shows that a bidirectional data exchange between semantic city models and SD models improves the predictions generated by the SD models. Furthermore, we show that automatic modification of the semantic city model by the output of the SD model allows for 3D visualization and further analysis of future scenarios.Since semantic 3D city models and SD models have complex data structures, and since the models have evolved in very different domains, integrating the models is a complex task. In order to facilitate the integration process, we developed a conceptual model, which describes the data structures of the semantic city model and of the SD model as well as the bidirectional relations between the models using the concept of model weaving. The approach was tested using the SD tool Vensim and a CityGML data set from the city of Munich for an urban densification use case.

In the land surveying profession fast changes have been taking place in the last fifty years. Technological changes are generated by the Information and Communication Technologies; the analogue – digital trends; the automatic data acquisition methods replace manual ones; instead of two-dimensional base maps we use dynamic spatial databases more and more integrated into a global data infrastructure. However, these changes cause impacts also on scientific level. The traditional top-down approach substituted by bottom-up methodologies; in many cases the point-by-point measurement is changed by 3D laserscanning or Unmanned Aerial Systems, which produces huge amount of data, but it needs new algorithms for information extraction; instead of a simple data provision land surveyors support complex spatial decisions. The paper is dealing with some aspects of these changes. In the first chapter the authors would like to highlight the “data-information-knowledge” relations and the importance of changes in professional education. The second chapter gives an example of the benefits of a Global Spatial Data Infrastructure in spatial decision support. Finally we introduce a new concept (Building Information Modelling) in modelling the real world. However, until now BIM is used in building construction industry, it can can be a paradigm shift in geospatial information management in general.

There is an increasing impetus for the use of digital city models and sensor network data to understand the current demand for utility resources and inform future infrastructure service planning across a range of spatial scales. Achieving this requires the ability to represent a city as a complex system of connected and interdependent components in which the topology of the electricity, water, gas, and heat demand-supply networks are modelled in an integrated manner. However, integrated modelling of these networks is hampered by the disparity between the predominant data formats and modelling processes used in the Geospatial Information Science (GIS) and Building Information Modelling (BIM) domains. This paper presents a software systems approach to scale-free, multi-format, integrated modelling of evolving cross-domain utility infrastructure network topologies, and the analysis of the spatiotemporal dynamics of their resource flows. The system uses a graph database to integrate the topology of utility network components represented in the CityGML UtilityNetwork Application Domain Extension (ADE), Industry Foundation Classes (IFC) and JavaScript Object Notation (JSON) real-time streaming messages. A message broker is used to disseminate the changing state of the integrated topology and the dynamic resource flows derived from the streaming data. The capability of the developed system is demonstrated via a case study in which internal building and local electricity distribution feeder networks are integrated, and a real-time building management sensor data stream is used to simulate and visualise the spatiotemporal dynamics of electricity flows using a dynamic web-based visualisation.

The city management is increasingly supported by information technologies, leading to paradigms such as smart cities, where decision-makers, companies and citizens are continuously interconnected. 3D modelling turns of great relevance when the city has to be managed making use of geospatial databases or Geographic Information Systems. On the other hand, laser scanning technology has experienced a significant growth in the last years, and particularly, terrestrial mobile laser scanning platforms are being more and more used with inventory purposes in both cities and road environments. Consequently, large datasets are available to produce the geometric basis for the city model; however, this data is not directly exploitable by management systems constraining the implementation of the technology for such applications. <br><br> This paper presents a new algorithm for the automatic detection of zebra crossing. The algorithm is divided in three main steps: road segmentation (based on a PCA analysis of the points contained in each cycle of collected by a mobile laser system), rasterization (conversion of the point cloud to a raster image coloured as a function of intensity data), and zebra crossing detection (using the Hough Transform and logical constrains for line classification). After evaluating different datasets collected in three cities located in Northwest Spain (comprising 25 strips with 30 visible zebra crossings) a completeness of 83% was achieved.

Abstract. The purpose of this research is to develop an approach for a Spatial Decision Support System (SDSS) that integrates Geographic Information Systems (GIS), Automated Machine Learning (AutoML), and Hyperparameter Optimization (HPO) to generate precision geo-interventions based on standardized geospatial data and user design constraints. The geo-intervention generation approach involves three steps: (1) Geo-binning, (2) AutoML, and (3) Prediction Optimization. Geo-binning is used to standardize geospatial data into regularized grids as inputs into AutoML models. Prediction optimization generates geo-interventions by applying user-design constraints and optimizing AutoML model output to find optimized input variables that form precise geo-interventions. An experiment in reducing road traffic collisions using infrastructural changes in Toronto, Ontario, Canada was done to evaluate the geo-intervention generation approach. The results of the experiment found that changing the number of schools, red light cameras, and transit shelters in high traffic areas could potentially halve the total number of traffic collisions according to a 80 by 80 geo-binned grid Auto-Sklearn model with a Mean Absolute Error (MAE) of 117.68. It was also found that user design constraints heavily affected the prediction optimization step as when the areas were altered to an alternative grid of cells with scarce infrastructure, the number of predicted collisions rose by 6127 collisions. Thus, limitations of this study included subjectivity in user design constraints, scalability, and interactivity. Future work involves improving modelling/optimization efficiency and developing an interactive interface for exploring generated precision geo-interventions.

A review of descriptive and genetic models is presented for unconformity-type uranium deposits with particular attention given to spatial representations of key process components of the mineralising system and their mappable expressions. This information formed the basis for the construction of mineral potential models for the world’s premier unconformity-style uranium provinces, the Athabasca Basin in Saskatchewan, Canada (>650,000 t U3O8), and the NW McArthur Basin in the Northern Territory, Australia (>450,000 t U3O8). A novel set of ‘edge’ detection routines was used to identify high-contrast zones in gridded geophysical data in support of the mineral potential modelling. This approach to geophysical data processing and interpretation offers a virtually unbiased means of detecting potential basement structures under cover and at a range of scales. Fuzzy logic mineral potential mapping was demonstrated to be a useful tool for delineating areas that have high potential for hosting economic uranium concentrations, utilising all knowledge and incorporating all relevant spatial data available for the project area. The resulting models not only effectively ‘rediscover’ the known uranium mineralisation but also highlight several other areas containing all of the mappable components deemed critical for the accumulation of economic uranium deposits. The intelligence amplification approach to mineral potential modelling presented herein is an example of augmenting expert-driven conceptual targeting with the powerful logic and rationality of modern computing. The result is a targeting tool that captures the current status quo of geospatial and exploration information and conceptual knowledge pertaining to unconformity-type uranium systems. Importantly, the tool can be readily updated once new information or knowledge comes to hand. As with every targeting tool, these models should not be utilised in isolation, but as one of several inputs informing exploration decision-making. Nor should they be regarded as ‘treasure maps’, but rather as pointers towards areas of high potential that are worthy of further investigation.

Abstract. Geographic information system (GIS) is known traditionally for the modelling of two-dimensional (2D) geospatial analysis and therefore present information about the extensive spatial framework. On the other hand, building information modelling (BIM) is digital representation of building life cycle. The increasing use of both BIM and GIS simultaneously because of their mutual relationship, as well as their similarities, has resulted in more relationships between both worlds, therefore the need for their integration. A significant purpose of these similarities is importing BIM data into GIS to significantly assist in different design-related issues. However, currently this is challenging due to the diversity between the two worlds which includes diversity in coordinate systems, three-dimensional (3D) geometry representation, and semantic mismatch. This paper describes an algorithm for the conversion of IFC data to CityGML in order to achieve the set goal of sharing information between BIM and GIS domains. The implementation of the programme developed using python was validated using an IFC model (block HO2) of a student’s hostel, Kolej Tun Fatima (KTF). The conversion is based on geometric and semantic information mapping and the use of 3D affine transformation of IFC data from local coordinate system (LCS) to CityGML world coordinate system (WCS) (EPSG:4236). In order to bridge the gap between the two data exchange formats of BIM and GIS, we conducted geometry and semantic mapping. In this paper, we limited the conversion of the IFC model on level of details 2 (LOD2). The conversion will serve as a bridge toward the development of a software that will perform the conversion to create a strong synergy between the two domains for purpose of sharing information.

AbstractModelling the spatial variations of a specific Global Geopotential Model (GGM) over a spatial area is important to enhance its local performance in Global Navigation Satellite Systems (GNSS) surveying. This study aims to investigate the potential of utilizing some of Geographic Information Systems (GIS) geospatial analysis tools, particularly Geographically Weighted Regression (GWR), in geoid modelling for the first time in Egypt as a case study. Its main target is developing an optimum regression method to be applied in spatial modelling of the deviations of a specific GGM (e. g., PGM17). Using a precise local geodetic dataset of 803 GPS/levelling stations, PGM17 undulation differences have been modelled using different regression techniques to evaluate their precision and accuracy. Based on investigating 13 possible regression formulas of probable combinations of independent variables, results showed that the PGM17 discrepancies over Egypt depend mostly on the terrain heights and geoidal undulations. Over 80 checkpoints, the attained variations between the GWR model and known values varied from −0.574 m to 0.500 m, with a mean of 0.001 m and a standard deviation equals ±0.205 m. Based on available data, it has been found that GWR improved the PGM17 deviations by 9 % in terms of standard deviation and by 98 % in terms of the mean. Additionally, the study generates a reasonably innovative product for the local geodetic community by building an enhanced version of the PGM17. This surface will be a precious resource in GNSS surveying in Egypt for heights conversion, leading to considerable cost reduction in civil engineering works and mapping projects.

Land use changes play an important role in interactions between human and physical systems, and have significant impacts on the environment at local, regional and global scales. Land use change is a complex process and so developing dynamic models to represent the process is a challenging task. Decision Trees (DT) is a Machine Learning (ML) method with the capability to extract trends and generate a representative model using historical geospatial data. While DT is used in remote sensing as an image classification method, it is not sufficiently examined in land use science. The main objective of this research study is to examine the capability of DT method to model urban land use change. Various numbers of attributes for three municipalities in the City of Belgrade, Republic of Serbia were used. Land use is represented with nine land use classes for three different time instances for the years 2003, 2007 and 2011. The kappa statistics and weighted Area Under Receiver Operating Characteristic Curve (AUC) were used to compare the model outputs with real land use datasets for year 2011. The maximum obtained values for kappa and weighted AUC indicate that DT is a useful method for modelling urban land use change. Furthermore, the derived classification tree generates information about the relationship between the considered causal factors and land use changes and allows for better understanding of the change process.

<p><strong>Abstract.</strong> Grist mills are structures in which stone wheels are used to grind grain (e.g. corn, wheat) into a powder-like form for human consumption. Circular stone wheels provided the pressure to grind the grain and separate seed components. In most grist mills the energy for turning the heavy stones was derived from water power. The most visible part of some these mills was the externally mounted water wheel. However, other common configurations of the water ‘turbine’ internal to the structure were used (Figure 1). While grist mills existed in Europe at least as early as the 11th century such mills were not present in the United States until the last 1600s. No comprehensive census has been conducted for grist mills in the U.S. although an estimate of all water powered mills in eastern U.S. was 65,000 in 1840. Many of these water powered mills in the census estimate were for textile, sawmills, manufacturing, and other non-grain-grinding applications.</p><p>Most often, the mill site included a small impoundment for creating a reliable source of water during low-flow stream conditions. These ponds were created in the late 1700s and especially in the 1800s as a reliable water source for turning grist or saw mills. By the middle of the 20th century the grist mills had all but vanished. Except for a few historic relics and conversion to touristic sites, the mill infrastructure has disappeared on the landscape while the mill ponds typically remain, serving other purposes, such as fishing or hunting lakes, or merely aesthetic environments.</p><p>The local ecosystems around the mills sites have been artificially modified by the presence of these stream impoundments providing a new environment for fish, mammals, avian species, and of course, humans. While numerous positive ecosystem values may be observed from their continued presence, the risk of dam failure to both downstream systems and humans is substantial. In fact, such a catastrophic series of dam failures (51 in South Carolina) occurred in October of 2015 from a heavy rainfall event. In other instances the impoundments now store toxic sediment originating from sources farther upstream such as mining, military or industrial processes.</p><p>Where are these historic mill sites and mill ponds? No inventory of mills or their ponds exist in South Carolina (and only for a few northeastern states). In this research we developed a systematic approach for identifying and mapping historic mill sites and ponds using a geohistorical framework and applied to South Carolina. The approach relied on 1) numerous geospatial sources, and 2) an analytical model of confidence mapping for predicting sites. The developed approach resulted in a new database of definitive and likely historic mill sites and mill pond locations throughout South Carolina. The sources of geospatial information was largely cartographic in nature including the following:</p><ul><li>USGS Historic Topographic Map Series</li><li>Robert Mills 1820 atlas covering South Carolina</li><li>Historic aerial photography</li><li>National Hydrography Data (NHD)</li><li>National Wetlands Inventory (NWI)</li><li>National Inventory of Dams</li><li>SC Department of Health and Environmental Control for Regulated Dams</li><li>Toponyms from the Geographic Names Information System (GNIS)</li><li>U.S. Census TIGER street data</li></ul><p>The design for a predictive model (Figure 2) using diverse historic data with various scales and reliability is problematic. Most of the grist mills have disappeared from the landscape and do not exist in contemporary geospatial databases. Historic maps might include toponyms for the sites or ponds but their map projection, scale, and subsequent distortions in geometry are problematic. In addition, some of the sources for data are derived from a similar source and thus, are not statistically independent. The design of the analytical model for mapping confidence in mill site locations was calibrated and subsequently validated using several independent sources of information. Numerous field visits to known and suspected locations for mills sites were conducted throughout the physiographic regions of South Carolina.</p><p>The model of likelihood included factors for the reliability of the source, the mapping scale and spatial accuracy of the source, correlation with other sources, and likelihood of correspondence with grist mills.</p>

Climate change poses a threat to cities. Geospatial information and communication technology (Geo-ICT) assisted planning is increasingly being utilised to foster urban sustainability and adaptability to climate change. To fill the theoretical and practical gaps of urban adaptive planning and Geo-ICT implementation, this article presents an urban ecosystem vulnerability assessment approach using integrated socio-ecological modelling. The application of the Geo-ICT method is demonstrated in a specific case study of climate-resilient city development in Nanjing (China), aiming at helping city decision-makers understand the general geographic data processing and policy revision processes in response to hypothetical future disruptions and pressures on urban social, economic, and environmental systems. Ideally, the conceptual framework of the climate-resilient city transition proposed in this study effectively integrates the geographic data analysis, policy modification, and participatory planning. In the process of model building, we put forward the index system of urban ecosystem vulnerability assessment and use the assessment result as input data for the socio-ecological model. As a result, the model reveals the interaction processes of local land use, economy, and environment, further generating an evolving state of future land use in the studied city. The findings of this study demonstrate that socio-ecological modelling can provide guidance in adjusting the human-land interaction and climate-resilient city development from the perspective of macro policy. The decision support using urban ecosystem vulnerability assessment and quantitative system modelling can be useful for urban development under a variety of environmental change scenarios.

Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). Nizwa city is a focal point of aflaj that underlies the historical relationship between ecology, economic dynamics, agricultural systems, and human settlements. Three hyperparameter algorithms, grid search (GS), random search (RS), and Bayesian optimisation, were used to optimise the parameters of the XGB model. Sentinel-2 and light detection and ranging (LiDAR) data via geographical information systems (GIS) were employed to derive variables of land use/land cover, and hydrological, topographical, and geological factors. The groundwater aflaj potential maps were categorised into five classes: deficient, low, moderate, high, and very high. Based on the evaluation of accuracy in the training stage, the following models showed a high level of accuracy based on the area under the curve: Bayesian-XGB (0.99), GS-XGB (0.97), RS-XGB (0.96), SVM (0.96), and XGB (0.93). The validation results showed that the Bayesian hyperparameter algorithm significantly increased XGB model efficiency in modelling groundwater aflaj potential. The highest percentages of groundwater potential in the very high class were the XGB (10%), SVM (8%), GS-XGB (6%), RS-XGB (6%), and Bayesian-XGB (6%) models. Most of these areas were located in the central and northeast parts of the case study area. The study concluded that evaluating existing groundwater datasets, facilities, current, and future spatial datasets is critical in order to design systems capable of mapping groundwater aflaj based on geospatial and ML techniques. In turn, groundwater protection service projects and integrated water source management (IWSM) programs will be able to protect the aflaj irrigation system from threats by implementing timely preventative measures.

Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering method for route planning and trajectory anomalies detection, which are the essential part of auto-vessel system design and development. In this paper, we present the development of an enhanced density-based spatial clustering (DBSCAN) method that can be applied on historical or real-time Automatic Identification System (AIS) data, so that vessel routes can be modelled, and the trajectories’ anomalies can be detected. The proposed methodology is based on developing an optimized trajectory clustering approach in two stages. Firstly, to increase the attribute dimension of the vessel’s positioning data, therefore other characteristics such as velocity and direction are considered in the clustering process along with geospatial information. Secondly, the DBSCAN clustering model has been enhanced by introducing the Mahalanobis Distance metric considering the correlations of the position cluster points aiming to make the identification process more accurate as well as reducing the computational cost.

<p><strong>Abstract.</strong> New urban strategies encourage compact city and higher density urban development due to unprecedented city growth and rapid urbanisation. This has led to greater attention to multi-dimensional representation, modelling and analytics of urban settings among urban planners, decision makers, and researchers. Nowadays, urban planning and urban design practitioners and scholars leverage the advancements in computer technology and multi-dimensional visualisation in examining the development scenarios from physical, environmental, social, and economic aspects. However, many urban planners still rely on two-dimensional (2D) land information and urban designers use three-dimensional (3D) graphic-based engines to asses a proposed building or assess the impact of changing development regulations. This limits the decision makers from a holistic approach through integrating the urban systems with other application domains such as transport, environmental, and disaster management to ensure the liveability of cities. This paper describes the design, and development of a multi-dimensional and spatially enabled platform to support liveability planning in Singapore. A Quantitative Urban Environment Simulation Tool (QUEST), developed in Singapore, leveraged 3D mapping data captured under the Singapore Land Authority’s (SLA) 3D National Topographic Mapping project. SLA's 3D data including Building Information Model (BIM), CityGML, and other geospatial data (building footprints and land use) were processed and adapted as a service for a series of urban analytics. The paper concludes that the prerequisites for any urban environmental simulation system to be integrated with other application domains are 3D mapping data and a digital urban model, which must be spatially accurate and based on open data standards.</p>

In both the Geospatial (Geo) and Building Information Modelling (BIM) domains, it is widely acknowledged that the integration of geo-data and BIM-data is beneficial and a crucial step towards solving the multi-disciplinary challenges of our built environment. The result of this integration—broadly termed GeoBIM—has the potential to be particularly beneficial in the context of the construction of large infrastructure projects, which could make use of data relating to the larger spatial extents typically handled in geographical information systems (GIS) as well as the detailed models generated by BIM. To date, GeoBIM integration has mainly been explored for buildings, in a 3D context and for small projects. This paper demonstrates the results of the next level of integration, exploring the addition of the fourth dimension by linking project schedule information to create 4D GeoBIM, examining interoperability challenges and benefits in the context of a number of use cases relating to the enabling works for a major commercial infrastructure project. The integrating power of location and time—knowing where and when data relate to—allows us to explore data interoperability challenges relating to linking real world construction data, created using commercial software, with other data sources; we are then able to demonstrate the benefits of 4D GeoBIM in the context of three decision making scenarios: examining the potential for prioritisation of noise mitigation interventions by identifying apartments closest to the noisiest construction process; development of a 4D location-enabled risk register allowing, for example, work to continue underground if a risk is specific to the top of a building; ensuring construction safety by using 3D buffering to ensure that the required distances between moving construction equipment and surrounding infrastructure are not breached. Additionally, once integrated, we are able to ‘democratize’ the data—make it accessible beyond the BIM and GIS expert group—by embedding it into a 3D/4D open source Web GIS tool.

<p><strong>Abstract.</strong> <strong>Spatial data infrastructure</strong></p><p> No mapping agency can expect to capture and process entirely by itself, all the geospatial data needed for its products. The agency needs data sets from elsewhere and workflows and protocols for creating its various products. Unsurprisingly, such workflows and inter-institutional arrangements have evolved into broader collaborations, particularly as <i>spatial data infrastructures (SDIs)</i>. An SDI is an evolving concept about facilitating and coordinating the exchange and sharing of geospatial data and services between stakeholders from different levels in the geospatial data community.</p><p> The Commission on SDI &amp; Standards (and its predecessors) of the International Cartographic Association (ICA) has developed formal models of an SDI, using the viewpoints of the <i>Reference Model for Open Distributed Processing (RM-ODP)</i> and the <i>Unified Modeling Language (UML)</i> for the detailed modelling. The Commission described an SDI from the <i>Enterprise Viewpoint</i> (purpose, scope and policies for an SDI), <i>Information Viewpoint</i> (semantics of information and information processing in an SDI) and <i>Computational Viewpoint</i> (functional decomposition of the SDI into a set of services that interact through interfaces) (Hjelmager et al., 2008, Cooper et al., 2012). The ICA Commission did not investigate the <i>Engineering</i> and <i>Technology Viewpoints</i> of an SDI, because they are implementation-specific and the Commission has aimed at providing technology-independent models for an SDI. Other researchers have used the Engineering and Technology Viewpoints for specific SDIs.</p><p><strong>SDI stakeholders</strong></p><p>The Enterprise Viewpoint model (Hjelmager et al., 2008) included six types of <i>stakeholders</i> in an SDI: <i>Policy Maker</i>, <i>Producer</i>, <i>Provider</i>, <i>Broker</i>, <i>Value-added Reseller</i> and <i>End User</i>. Any one person, group, committee or organisation can have multiple stakeholder roles, which could be simultaneously. Each stakeholder can have an active or passive relationship with any activities or components in an SDI. A stakeholder in an SDI could also be considered to be an <i>actor</i> (Oliveira and Lisboa-Filho, 2015). Various trends are increasing the number of stakeholders in the SDI, their associated diversity and heterogeneity, and the resources at their disposal.</p><p>Subsequently, the ICA Commission assessed whether or not these SDI models catered for <i>volunteered geographical information (VGI)</i>, that is, user-generated geospatial content, or user-generated content with geospatial components. The models are sufficiently robust to do so, though it was then realised that the SDI stakeholders needed to be described in more detail as <i>subtypes</i> of the general roles of the six types of SDI stakeholders. The Commission then identified 24 subtypes for the stakeholders, with several of these subtypes having further sub-subtypes. For example, a <i>Producer</i> could have a subtype <i>Status</i>, which in turn could have the subtypes <i>Official Mapping Agency</i> or <i>Commercial Mapping Agency</i> (Cooper et al., 2011). However, a better term than subtype might be specialization, special case, attribute, activity or role.</p><p> Collectively, these Enterprise, Information and Computational Viewpoints and the detailed stakeholders form what is known colloquially in the literature as the ICA <i>model</i> or <i>ICA’s formal model</i> of an SDI, such as by (Oliveira et al., 2016). The ICA SDI model is not meant to be a <i>prescriptive</i> model of an SDI, but rather a <i>descriptive</i> one. The ICA SDI model is also implementation-independent and hence somewhat abstract.</p><p><strong>Issues with the stakeholders</strong></p><p> While all the stakeholder subtypes in an SDI were given definitions, some of the types and subtypes were confused with one another by other researchers. Further, it became clear that not all stakeholders have benevolent relationships with SDIs, whether the stakeholders be malevolent, or just too idle or incompetent to help the SDI succeed. Hence, it is necessary to update the ICA SDI stakeholder model.</p><p>We have reviewed the literature and several authors have proposed improvements to the ICA SDI stakeholder model and have highlighted parts of the model that are not well understood. Further, the ICA Commission itself has also revisited the model at previous meetings. The key contributions concerning the stakeholder model are summarised in this paper. They include:</p> <ul><li>Making it clear that any contributor to an SDI should control their assets in the SDI and can withdraw them when they see fit. </li><li>Catering explicitly for a contact or representative for any community. </li><li>Including an educator role, and by extension, a researcher role. </li><li>Including a role for those who fund the SDI itself and fund all of the relevant stakeholders so that they can function effectively. </li><li>Catering for how stakeholders could be assembled together, such as in special-interest groups. </li><li>Catering for oversight explicitly, such as through an ombud. </li><li>Making it clear that a subtype of a stakeholder is not necessarily subordinate to that stakeholder. </li><li>Characterising the maturity of a stakeholder. </li><li>Including a subtype for public-sector producers that are not official mapping agencies. </li><li>Whether or not agency, contracting or representation roles or functions should be deemed to be separate stakeholders in the model. </li><li>Improving the End User, which has only two subtypes. </li><li>Catering for shifts in the roles and characteristics of SDI stakeholders over time. </li><li>Including service producers explicitly. </li><li>Catering for representatives of the SDI who liaise with other organisations or other SDIs. </li><li>Including explicitly some of the roles within the Secretariat, such as systems administration, technical support and quality assurance. </li><li>Some attributes could be common across all or most of the six types of stakeholders in an SDI, such as their motivation for contributing to, or using, any particular SDI; their authority or ability to contribute; whether or not they are liable for their contributions and any consequences thereof; and the ethics related to the contribution, including invasion of privacy, arbitrary restrictions or mischief. </li><li>The SDI stakeholder model needs to cater for both the top-down and the bottom-up approaches to an SDI.</li></ul> <p>This paper proposes some revisions and expansions on this ICA model of the stakeholders in a spatial data infrastructure. While updating the model has not yet been completed, we believe that it will be appropriate to present our findings now to the ICA community to obtain feedback.</p>

Abstract. The current process of issuing planning permits mostly relies on checking Land-use Regulations (LuRs) against two-dimensional (2D) analogue or digital proposed development plans. Checking three-dimensional (3D) LuRs within 2D proposed development plans results in challenges for decision-makers to understand LuRs’ limits and the impacts of the proposed developments on existing buildings in their surrounded proximity. Given the advancement of 3D geospatial technologies, to overcome such challenges and facilitate the process of issuing planning permits, 3D digital approaches should be developed for effective 3D storage, analysis, and visualisation of 3D LuRs and detection of their potential conflicts. This paper, as part of an internship project with Bentley systems, aims to design and develop a web-based 3D visualisation prototype called iTwin4PP for issuing planning permits using Bentley iTwin platform. This prototype first demonstrates how 3D LuRs related to planning approval can be modelled automatically in 3D and combined with an integrated BIM-GIS environment including BIM designs of the proposed developments and GIS models of planning/city-data. Then, the prototype considers the possibility of 3D spatial analyses (especially proximity analysis) for verifying 3D LuRs automatically to detect potential spatio-semantic conflicts that may arise between modelled LuRs and physical/planning objects. Five LuRs subject to planning approval in Victorian jurisdiction, in Australia, including height limits, energy efficiency protection, overshadowing open space, noise impacts, and overlooking are highlighted. While these LuRs are specific to Melbourne’s planning scheme ordinance, we believe that the prototype and encountered challenges in integrating different sources of information especially BIM and GIS, modelling 3D LuRs, and detecting their potential conflicts are common and can be applied in other jurisdictions.

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.

Since large amount of geospatial data are produced by various sources and stored in incompatible formats, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. First, we provide a uniform integration paradigm for users to retrieve geospatial data. Then, we align the retrieved geospatial data in the modeling process to eliminate heterogeneity with the help of Karma. Our main contribution focuses on addressing the third problem. Previous work has been done by defining a set of semantic rules for performing the linking process. However, the geospatial data has some specific geospatial relationships, which is significant for linking but cannot be solved by the Semantic Web techniques directly. We take advantage of such unique features about geospatial data to implement the linking process. In addition, the previous work will meet a complicated problem when the geospatial data sources are in different languages. In contrast, our proposed linking algorithms are endowed with translation function, which can save the translating cost among all the geospatial sources with different languages. Finally, the geospatial data is integrated by eliminating data redundancy and combining the complementary properties from the linked records. We mainly adopt four kinds of geospatial data sources, namely, OpenStreetMap(OSM), Wikmapia, USGS and EPA, to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. 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Este trabalho apresenta uma analise da variação da linha de costa na região da Ilha dos Guarás, Município de Mariteua-PA ao longo das ultimas 3 décadas. Foi analisada uma seqüência de imagens de satélite de alta resolução da área e estabelecido um projeto em Sistema de Informações geográficas (SIG) visando a identificação e caracterização da linha de costa e estabelecendo padrões de mudanças. Os caso estudado mostrou que ferramentas geoespaciais como o “digital shoreline analyst” (DAS) podem ferramentas ser de grande importância na manipulação, análise e modelagem de dados costeiros, principalmente aplicados a estudos de conservação e monitoramento. A Ilha dos Guarás, em especial, apresentou um comportamento bastante peculiar com uma tendência de acreção nas porções laterais (Leste e Oeste) em oposição a uma tendência menos clara de erosão na porção central. Tais processos parecem ter sido mais determinantes na conformação da estrutura costeira da Ilha até o inicio da década de 2000 quando se tornou consideravelmente menos intenso. Palavras Chave: Linha de costa, sensoriamento remoto, dinâmica costeira, modelagem Coastline Changes Using Sattelite Images time series in Guarás Island Region – Pará State, Brazil ABSTRACTThis work analyzes the coastline changes in the Ilha dos Guarás, Para State, Brazil. A sequence high resolution satellite images from 1985 to 2011 of the areas were analyzed in order to develop a systematic Geographic Information Systems (GIS) approach for the identification and characterization of the shoreline characteristics and how they change over the time. The main goal of our work is the proposal of a unified database to incorporate both spectral and spatial data in a temporal GIS framework. The examples analyzed showed that geospatial tools such as the Digital Shoreline Analyst (DSA) used could became a powerful tool for handling and analyzed data focused on the environmental monitoring and the coastal protection and conservation. The Guarás Island showed a peculiar dynamic with two areas of intense accretion in the flanks of the island in opposition of the central portion of the island submitted to a unstable erosional process. These processes were more severe until the beginning of the 21th century when it became considerable less intense. Keywords: Coastline, Remote Sensing, Coastal dynamics, shoreline modelling

In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

Geospatial datacubes are the database backend of novel types of spatiotemporal decision-support systems employed in large organizations. These datacubes extend the datacube concept underlying the field of Business Intelligence (BI) into the realm of geospatial decision-support and geographic knowledge discovery. The interoperability between geospatial datacubes facilitates the reuse of their content. Such interoperability, however, faces risks of data misinterpretation related to the heterogeneity of geospatial datacubes. Although the interoperability of transactional databases has been the subject of several research works, no research dealing with the interoperability of geospatial datacubes exists. In this paper, the authors support the semantic interoperability between geospatial datacubes and propose a categorization of semantic heterogeneity problems that may occur in geospatial datacubes. Additionally, the authors propose an approach to deal with the related risks of data misinterpretation, which consists of evaluating the fitness-for-use of datacubes models, and a general framework that facilitates making appropriate decisions about such risks. The framework is based on a hierarchical top-down structure going from the most general level to the most detailed level, showing the usefulness of the proposed approach in environmental applications.

Abstract. This study aims to improve the interoperability between models of geospatial information from the applications domains of Geographic Information Systems (GIS), Intelligent Transport Systems (ITS) and Building Information Models (BIM). A state-of-the-art analysis showed that the Unified Modelling Language (UML) and Model-Driven Architecture (MDA) are used for modelling information in a geospatial context in all three domains, but with different approaches and levels of formality. A structure of formal UML profiles for modelling of geospatial information in GIS, ITS and BIM is suggested and tested for implementation. The Core Geospatial Profile (GCP) and general encoding profiles for the Geography Markup Language (GML) and the Web Ontology Language (OWL) are based on adapted concepts from ISO/TC 211 standards. Community specific profiles for conceptual models and encodings are based on UML profiles and the use of UML for specific information models in the three application domains. The studies and related research showed that the structure of UML profiles could be implemented and used for information modelling in the UML software Enterprise Architect and that existing profiles and information models could be adapted into the framework. Integration of information models in a common approach based on MDA and UML establishes a fundament for improved interoperability through a shared understanding of the digital representation of the real world.

Geospatial data have a crucial role in several projects related to infrastructures and land management. GIS software are able to perform advanced geospatial analyses, but they lack several instruments and tools for parametric modelling typically available in BIM. At the same time, BIM software designed for buildings have limited tools to handle geospatial data. As things stand at the moment, BIM and GIS could appear as complementary solutions, notwithstanding research work is currently under development to ensure a better level of interoperability, especially at the scale of the building. On the other hand, the transition from the local (building) scale to the infrastructure (where geospatial data cannot be neglected) has already demonstrated that parametric modelling integrated with geoinformation is a powerful tool to simplify and speed up some phases of the design workflow. This paper reviews such mixed approaches with both simulated and real examples, demonstrating that integration is already a reality at specific scales, which are not dominated by “pure” GIS or BIM. The paper will also demonstrate that some traditional operations carried out with GIS software are also available in parametric modelling software for BIM, such as transformation between reference systems, DEM generation, feature extraction, and geospatial queries. A real case study is illustrated and discussed to show the advantage of a combined use of both technologies. BIM and GIS integration can generate greater usage of geospatial data in the AECOO (Architecture, Engineering, Construction, Owner and Operator) industry, as well as new solutions for parametric modelling with additional geoinformation.

Purpose The purpose of this paper is to explain the rise of geospatial data, its importance for business and some of the problems associated with its development and use. Design/methodology/approach The paper reviews a certain amount of previously published literature but is based mainly on analysis of the very large number of responses to a consultation paper on geospatial data published by the UK Government. Findings The findings are that while there is strong appreciation of the potential benefits of using geospatial data, there are many barriers to the development, sharing and use of geospatial data, ranging from problems of incompatibility in data definitions and systems to regulatory issues. The implication for governments and for providers and users of geospatial data relates to the need to take a long-term approach to planning in resolving the issues identified. Research limitations/implications The research findings are limited to the UK, but similar findings would be likely in any other large Western country. Practical implications This paper confirms the need for a strong and coherent approach to the planning of geospatial data and systems for the establishment of a clear basis for the different parties to work together and the need to clearly separate the roles of the government in establishing frameworks and standards and the role of the private sector in developing applications and solutions. Social implications Society is increasingly dependent on the use of geospatial data, in improving living standards and dealing with social problems. The recommendations identified in this paper, if followed, will facilitate these improvements. Originality/value The value of this paper is the tight synthesis that it provides of a wide ranging and complex range of responses to the UK Government consultation and placing these responses in the wider context of the development of geospatial data.

For many years FIG, the International Association of Surveyors, has been trying to bridge the gap between surveyors and the geospatial society as a whole, with the geospatial industries in particular. Traditionally the surveying profession contributed to the good of society by creating and maintaining highly precise and accurate geospatial data bases, based on an in-depth knowledge of spatial reference frameworks. Furthermore in many countries surveyors may be entitled to make decisions about land divisions and boundaries. By managing information spatially surveyors today develop into the role of geo-data managers, the longer the more. Job assignments in this context include data entry management, data and process quality management, design of formal and informal systems, information management, consultancy, land management, all that in close cooperation with many different stakeholders. Future tasks will include the integration of geospatial information into e-government and e-commerce systems. The list of professional tasks underpins the capabilities of surveyors to contribute to a high quality geospatial data and information management. In that way modern surveyors support the needs of a geo-spatial society. The paper discusses several approaches to define the role of the surveyor within the modern geospatial society.

For many years FIG, the International Association of Surveyors, has been trying to bridge the gap between surveyors and the geospatial society as a whole, with the geospatial industries in particular. Traditionally the surveying profession contributed to the good of society by creating and maintaining highly precise and accurate geospatial data bases, based on an in-depth knowledge of spatial reference frameworks. Furthermore in many countries surveyors may be entitled to make decisions about land divisions and boundaries. By managing information spatially surveyors today develop into the role of geo-data managers, the longer the more. Job assignments in this context include data entry management, data and process quality management, design of formal and informal systems, information management, consultancy, land management, all that in close cooperation with many different stakeholders. Future tasks will include the integration of geospatial information into e-government and e-commerce systems. The list of professional tasks underpins the capabilities of surveyors to contribute to a high quality geospatial data and information management. In that way modern surveyors support the needs of a geo-spatial society. The paper discusses several approaches to define the role of the surveyor within the modern geospatial society.

This article describes how geographic information systems (GISs) can enable, enrich and enhance geospatial applications and services. Accurate calculation of the similarity among geospatial entities that belong to different data sources is of great importance for geospatial data linking. At present, most research works use the name or category of the entity to measure the similarity of geographic information. Although the geospatial relationship is significant for geographic similarity measure, it has been ignored by most of the previous works. This article introduces the geospatial relationship and topology, and proposes an approach to compute the geospatial record similarity based on multiple features including the geospatial relationships, category and name tags. In order to improve the flexibility and operability, supervised machine learning such as SVM is used for the task of classifying pairs of mapping records. The authors test their approach using three sources, namely, OpenStreetMap, Google and Wikimapia. The results showed that the proposed approach obtained high correlation with the human judgements.

Geospatial datasets are currently constructed, managed, and utilized individually according to the spatial scale of the real world, such as the ground/surface/underground or indoor/outdoor, as well the particular purpose of the geospatial data used for location-based services. In addition, LBS applications use an optimal data model and data format according to their particular purpose, and thus, various datasets exist to represent the same spatial features. Such duplicated geospatial datasets and geographical feature-based GIS data cause serious problems in the financial area, compatibility issues among LBS systems, and data integration problems among the various geospatial datasets generated independently for different systems. We propose a geospatial data fusion model called the topological relation-based data fusion model (TRDFM) using topological relations among spatial objects in order to integrate different geospatial datasets and different data formats. The proposed model is a geospatial data fusion model implemented in a spatial information application and is used to directly provide spatial information-based services without data conversion or exchange of geometric data generated by different data models. The proposed method was developed based on an extension of the AnchorNode concept of IndoorGML. The topological relationships among spatial objects are defined and described based upon the basic concept of IndoorGML. This paper describes the concept of the proposed TRDFM and shows an experimental implementation of the proposed data fusion model using commercial 3D GIS software. Finally, the limitations of this study and areas of future research are summarized.

Geospatial decision makers have to be aware of the varying interests of all stakeholders. One crucial task in the process is to identify relevant information available from the Web. In this chapter the authors introduce an application in the quarrying domain which integrates Semantic Web technologies to provide new ways to discover and reason about relevant information. The authors discuss the daily struggle of the domain experts to create decision-support maps helping to find suitable locations for opening up new quarries. After explaining how semantics can help these experts, they introduce the various components and the architecture of the software which has been developed in the European funded SWING project. In the last section, the different use cases illustrate how the implemented tools have been applied to real world scenarios.