1,395 research outputs found

    LiDAR data management pipeline; from spatial database population to web-application visualization

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    While the existence of very large and scalable Database Management Systems (DBMSs) is well recognized, it is the usage and extension of these technologies to managing spatial data that has seen increasing amounts of research work in recent years. A focused area of this research work involves the handling of very high resolution Light Detection and Ranging (LiDAR) data. While LiDAR has many real world applications, it is usually the purview of organizations interested in capturing and monitoring our environment where it has become pervasive. In many of these cases, it has now become the de facto minimum standard expected when a need to acquire very detailed 3D spatial data is required. However, significant challenges exist when working with these data sources, from data storage to feature extraction through to data segmentation all presenting challenges relating to the very large volumes of data that exist. In this paper, we present the complete LiDAR data pipeline as managed in our spatial database framework. This involves three distinct sections, populating the database, building a spatial hierarchy that describes the available data sources, and spatially segmenting data based on user requirements which generates a visualization of these data in a WebGL enabled web-application viewer. All work presented is in an experimental results context where we show how this approach is runtime efficient given the very large volumes of LiDAR data that are being managed

    Seeing the invisible: from imagined to virtual urban landscapes

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    Urban ecosystems consist of infrastructure features working together to provide services for inhabitants. Infrastructure functions akin to an ecosystem, having dynamic relationships and interdependencies. However, with age, urban infrastructure can deteriorate and stop functioning. Additional pressures on infrastructure include urbanizing populations and a changing climate that exposes vulnerabilities. To manage the urban infrastructure ecosystem in a modernizing world, urban planners need to integrate a coordinated management plan for these co-located and dependent infrastructure features. To implement such a management practice, an improved method for communicating how these infrastructure features interact is needed. This study aims to define urban infrastructure as a system, identify the systematic barriers preventing implementation of a more coordinated management model, and develop a virtual reality tool to provide visualization of the spatial system dynamics of urban infrastructure. Data was collected from a stakeholder workshop that highlighted a lack of appreciation for the system dynamics of urban infrastructure. An urban ecology VR model was created to highlight the interconnectedness of infrastructure features. VR proved to be useful for communicating spatial information to urban stakeholders about the complexities of infrastructure ecology and the interactions between infrastructure features.https://doi.org/10.1016/j.cities.2019.102559Published versio

    A Spatial Analysis of African Oil and Gas Infrastructure Security

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    Many countries and governments around the world rely on the production of oil and gas resources. The high cost of the assets and infrastructure used to produce these resources makes them a prime target for terrorist attack and theft. As a result, the security of oil and gas infrastructure is becoming exceedingly important. Both governments and private companies are interested in protecting the infrastructure used to extract, transport, and refine these resources in many places, including Africa. Geographic information systems (GIS) has the ability to assist with mapping infrastructure and assets, performing spatial analysis concerning areas of high-risk or vulnerability, and creating web-mapping systems that allow users to view and upload information as they acquire it. The end goal is to contribute to the protection of the oil and gas industry, and government’s dependent on this industry, from imminent and future threats

    GEOSPATIAL DATA PROCESSING FOR 3D CITY MODEL GENERATION, MANAGEMENT AND VISUALIZATION

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    Recent developments of 3D technologies and tools have increased availability and relevance of 3D data (from 3D points to complete city models) in the geospatial and geo-information domains. Nevertheless, the potential of 3D data is still underexploited and mainly confined to visualization purposes. Therefore, the major challenge today is to create automatic procedures that make best use of available technologies and data for the benefits and needs of public administrations (PA) and national mapping agencies (NMA) involved in “smart city” applications. The paper aims to demonstrate a step forward in this process by presenting the results of the SENECA project (Smart and SustaiNablE City from Above – http://seneca.fbk.eu). State-of-the-art processing solutions are investigated in order to (i) efficiently exploit the photogrammetric workflow (aerial triangulation and dense image matching), (ii) derive topologically and geometrically accurate 3D geo-objects (i.e. building models) at various levels of detail and (iii) link geometries with non-spatial information within a 3D geo-database management system accessible via web-based client. The developed methodology is tested on two case studies, i.e. the cities of Trento (Italy) and Graz (Austria). Both spatial (i.e. nadir and oblique imagery) and non-spatial (i.e. cadastral information and building energy consumptions) data are collected and used as input for the project workflow, starting from 3D geometry capture and modelling in urban scenarios to geometry enrichment and management within a dedicated webGIS platform

    Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

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    This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

    Bridging the Geospatial Education-Workforce Divide: A Case Study on How Higher Education Can Address the Emerging Geospatial Drivers and Trends of the Intelligent Web Mapping Era

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    The purpose of this exploratory collective case study is to discover how geospatial education can meet the geospatial workforce needs of the Commonwealth of Virginia, in the emerging intelligent web mapping era. Geospatial education uses geographic information systems (GIS) to enable student learning by increasing in-depth spatial analysis and meaning using geotechnology tools (Baker & White, 2003). Bandura’s (1977) self-efficacy theory and geography concept of spatial thinking form an integrated theoretical framework of spatial cognition for this study. Data collection included in-depth interviews of twelve geospatial stakeholders, documentation collection, and supporting Q methodology to determine the viewpoints of a total of 41 geospatial stakeholders. Q methodology is a type of data collection that when used as a qualitative method utilizes sorting by the participant to determine their preferences. Data analysis strategies included cross-case synthesis, direct interpretation, generalizations, and a correlation matrix to show similarities in participants\u27 preferences. The results revealed four collaborative perceptions of the stakeholders, forming four themes of social education, technology early adoption, data collaboration, and urban fundamentals. Four strategies were identified for higher education to prepare students for the emerging geospatial workforce trends. These strategies are to teach fundamentals, develop agile faculty and curriculum, use an interdisciplinary approach, and collaborate. These strategies reflect the perceptions of stakeholders in this study on how higher education can meet the emerging drivers and trends of the geospatial workforce
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