2,263 research outputs found

    Ontology based Scene Creation for the Development of Automated Vehicles

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    The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

    Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

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    Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure

    An Approach to Developing a Spatio-Temporal Composite Measure of Climate Change-Related Human Health Impacts in Urban Environments

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    Introduction: Rapid population growth along with an increase in the frequency and intensity of climate change-related impacts in costal urban environments emphasize the need for the development of new tools to help disaster planners and policy makers select and prioritize mitigation and adaptation measures. Using the concept of the resilience of a community, which is a measure of how rapidly the community can recover to its previous level of functionality following a disruptive event is still a relatively new concept for many engineers, planners and policy makers, but is becoming recognized as an increasingly important and some would argue, essential component for the development and subsequent assessment of adaptation plans being considered for communities at risk of climate change-related events. The holistic approach which is the cornerstone of resilience is designed to integrate physical, economic, health, social and organizational impacts of climate change in urban environments. This research presents a methodology for the development of a quantitative spatial and temporal composite measure for assessing climate change-related health impacts in urban environments. Methods: The proposed method is capable of considering spatial and temporal data from multiple inputs, relating to both physical and social parameters. This approach uses inputs such as the total population density and densities of various demographics, burden of diseases conditions, flood inundation mapping, and land use change for both historical and current conditions. The research has demonstrated that the methodology presented generates sufficiently accurate information to be useful for planning adaptive strategies. To assemble all inputs into a single measure of health impacts, a weighting system was assigned to apply various priorities to the spatio-temporal data sources. Weights may be varied to assess how they impact the final results. Finally, using spatio-temporal extrapolation methods the future behavior of the same key spatial variables can be projected. Although this method was developed for application to any coastal mega-city, this thesis demonstrates the results obtained for Metro Vancouver, British Columbia, Canada. The data was collected for the years 1981, 1986, 1991, 1996, 2001, 2006 and 2011, as information was readily available for these years. Fine resolution spatial data for these years was used in order to give a dynamic simulation of possible health impacts for future projections. Linear and auto-regressive spatio-temporal extrapolations were used for projecting a 2050’s Metro Vancouver health impact map (HIM). Conclusion: Results of this work show that the approach provides a more fully integrated view of the resilience of the city which incorporates aspects of population health. The approach would be useful in the development of more targeted adaptation and risk reduction strategies at a local level. In addition, this methodology can be used to generate inputs for further resilience simulations. The overall value of this approach is that it allows for a more integrated assessment of the city vulnerability and could lead to more effective adaptive strategies

    Geo-Spatial Analysis in Hydrology

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    Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale

    Development and Applications of Similarity Measures for Spatial-Temporal Event and Setting Sequences

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    Similarity or distance measures between data objects are applied frequently in many fields or domains such as geography, environmental science, biology, economics, computer science, linguistics, logic, business analytics, and statistics, among others. One area where similarity measures are particularly important is in the analysis of spatiotemporal event sequences and associated environs or settings. This dissertation focuses on developing a framework of modeling, representation, and new similarity measure construction for sequences of spatiotemporal events and corresponding settings, which can be applied to different event data types and used in different areas of data science. The first core part of this dissertation presents a matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This framework supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the modified Jaccard index with temporal order constraints and accommodates different event data types. This approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. These similarity measures are incorporated into a clustering method and successfully demonstrate the usefulness in a case study analysis of event sequences extracted from space time series of a water quality monitoring system. This dissertation further proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been considered. While modeling event setting sequences, spatial and temporal scales are considered to define the bounds of the setting and incorporate dynamic variables along with static variables. Using a matrix-based representation and an extended Jaccard index, new similarity measures are developed to allow for the use of all variable data types. With these similarity measures coupled with other multivariate statistical analysis approaches, results from a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, support the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. To test the scalability of STES similarity measure in a larger dataset and an extended application in different fields, this dissertation compares and contrasts the prospective space-time scan statistic with the STES similarity approach for identifying COVID-19 hotspots. The COVID-19 pandemic has highlighted the importance of detecting hotspots or clusters of COVID-19 to provide decision makers at various levels with better information for managing distribution of human and technical resources as the outbreak in the USA continues to grow. The prospective space-time scan statistic has been used to help identify emerging disease clusters yet results from this approach can encounter strategic limitations imposed by the spatial constraints of the scanning window. The STES-based approach adapted for this pandemic context computes the similarity of evolving normalized COVID-19 daily cases by county and clusters these to identify counties with similarly evolving COVID-19 case histories. This dissertation analyzes the spread of COVID-19 within the continental US through four periods beginning from late January 2020 using the COVID-19 datasets maintained by John Hopkins University, Center for Systems Science and Engineering (CSSE). Results of the two approaches can complement with each other and taken together can aid in tracking the progression of the pandemic. Overall, the dissertation highlights the importance of developing similarity measures for analyzing spatiotemporal event sequences and associated settings, which can be applied to different event data types and used for data mining, sequence classification, and clustering

    Flood Management Deep Learning Model Inputs: A Review of Necessary Data and Predictive Tools

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    Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural test site are presented subject to available data and input from key stakeholders. The transportation safety or disaster planner can use these results to begin integrating deep learning methods in their planning strategies based on region-specific geospatial data and information

    Proceedings of the 3rd Open Source Geospatial Research & Education Symposium OGRS 2014

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    The third Open Source Geospatial Research & Education Symposium (OGRS) was held in Helsinki, Finland, on 10 to 13 June 2014. The symposium was hosted and organized by the Department of Civil and Environmental Engineering, Aalto University School of Engineering, in partnership with the OGRS Community, on the Espoo campus of Aalto University. These proceedings contain the 20 papers presented at the symposium. OGRS is a meeting dedicated to exchanging ideas in and results from the development and use of open source geospatial software in both research and education.  The symposium offers several opportunities for discussing, learning, and presenting results, principles, methods and practices while supporting a primary theme: how to carry out research and educate academic students using, contributing to, and launching open source geospatial initiatives. Participating in open source initiatives can potentially boost innovation as a value creating process requiring joint collaborations between academia, foundations, associations, developer communities and industry. Additionally, open source software can improve the efficiency and impact of university education by introducing open and freely usable tools and research results to students, and encouraging them to get involved in projects. This may eventually lead to new community projects and businesses. The symposium contributes to the validation of the open source model in research and education in geoinformatics
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