Infrastructure Engineering - Research Publications

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    Translating Place-Related Questions to GeoSPARQL Queries
    Hamzei, E ; Tomko, M ; Winter, S (ACM, 2022-04-25)
    Many place-related questions can only be answered by complex spatial reasoning, a task poorly supported by factoid question retrieval. Such reasoning using combinations of spatial and non-spatial criteria pertinent to place-related questions is increasingly possible on linked data knowledge bases. Yet, to enable question answering based on linked knowledge bases, natural language questions must first be re-formulated as formal queries. Here, we first present an enhanced version of YAGO2geo, the geospatially-enabled variant of the YAGO2 knowledge base, by linking and adding more than one million places from OpenStreetMap data to YAGO2. We then propose a novel approach to translate the place-related questions into logical representations, theoretically grounded in the core concepts of spatial information. Next, we use a dynamic template-based approach to generate fully executable GeoSPARQL queries from the logical representations. We test our approach using the Geospatial Gold Standard dataset and report substantial improvements over existing methods.
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    Neural factoid geospatial question answering
    Li, H ; Hamzei, E ; Majic, I ; Hua, H ; Renz, J ; Tomko, M ; Vasardani, M ; Winter, S ; Baldwin, T (UNIV MAINE, 2021-01-01)
    Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.
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    The semantics of place-related questions
    Kuhn, W ; Hamzei, E ; Tomko, M ; Winter, S ; Li, H (UNIV MAINE, 2021-01-01)
    The trend to equip information systems with question-answering capabilities raises the design problem of deciding which questions a system should be able to answer. Typical solutions build on mining human conversations or logs from similar systems for question patterns. For the case of questions about geographic places, we present a complementary approach, showing how to derive possible questions from an ontology of spatial information and a classification of place facets. We argue that such an approach reduces the inherent and substantial data bias of current solutions. At a more general level, we provide a novel understanding of spatial questions and their role in designing and using spatial information systems.
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    Pose-aware monocular localization of occluded pedestrians in 3D scene space
    Rahimi, MM ; Khoshelham, K ; Stevenson, M ; Winter, S (Elsevier BV, 2021-12)
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    Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases
    Guth, J ; Keller, S ; Hinz, S ; Winter, S (UNIV MAINE, 2021-01-01)
    OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors of ten lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classifi cation errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability to suggest possible misclassifi cations to a human user. The methodology is applied to an exemplar case for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts, and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub.
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    Decentralized management of ephemeral traffic incidents
    Hu, W ; Chen, B ; Winter, S ; Khoshelham, K (Wiley, 2022-08-01)
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    Spatial Concepts in the Conversation With a Computer
    Winter, S ; Baldwin, T ; Tomko, M ; Renz, J ; Kuhn, W ; Vasardani, M (ASSOC COMPUTING MACHINERY, 2021-07-01)
    Conversing about places with a computer poses a range of challenges to current AI.
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    Spatial Causality: A Systematic Review on Spatial Causal Inference
    Akbari, K ; Winter, S ; Tomko, M (WILEY, 2021-12-14)
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    Investigating the practical viability of walk-sharing in improving pedestrian safety.
    Bhowmick, D ; Winter, S ; Stevenson, M ; Vortisch, P (Springer Science and Business Media LLC, 2021)
    Walk-sharing is a cost-effective and proactive approach that promises to improve pedestrian safety and has been shown to be technically (theoretically) viable. Yet, the practical viability of walk-sharing is largely dependent on community acceptance, which has not, until now, been explored. Gaining useful insights on the community's spatio-temporal and social preferences in regard to walk-sharing will ensure the establishment of practical viability of walk-sharing in a real-world urban scenario. We aim to derive practical viability using defined performance metrics (waiting time, detour distance, walk-alone distance and matching rate) and by investigating the effectiveness of walk-sharing in terms of its major objective of improving pedestrian safety and safety perception. We make use of the results from a web-based survey on the public perception on our proposed walk-sharing scheme. Findings are fed into an existing agent-based walk-sharing model to investigate the performance of walk-sharing and deduce its practical viability in urban scenarios.
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    Identification of parking spaces from multi-modal trajectory data
    Dey, S ; Winter, S ; Goel, S ; Tomko, M (WILEY, 2021-10-08)