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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    Target Word Masking for Location Metonymy Resolution
    Li, H ; Vasardani, M ; Tomko, M ; Baldwin, T (International Committee on Computational Linguistics, 2020)
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    Indoor LiDAR relocalization based on deep learning using a 3D model
    Zhao, H ; Acharya, D ; Tomko, M ; Khoshelham, K (Copernicus GmBH, 2020-08-06)
    Indoor localization, navigation and mapping systems highly rely on the initial sensor pose information to achieve a high accuracy. Most existing indoor mapping and navigation systems cannot initialize the sensor poses automatically and consequently these systems cannot perform relocalization and recover from a pose estimation failure. For most indoor environments, a map or a 3D model is often available, and can provide useful information for relocalization. This paper presents a novel relocalization method for LiDAR sensors in indoor environments to estimate the initial LiDAR pose using a CNN pose regression network trained using a 3D model. A set of synthetic LiDAR frames are generated from the 3D model with known poses. Each LiDAR range image is a one-channel range image, used to train the CNN pose regression network from scratch to predict the initial sensor location and orientation. The CNN regression network trained by synthetic range images is used to estimate the poses of the LiDAR using real range images captured in the indoor environment. The results show that the proposed CNN regression network can learn from synthetic LiDAR data and estimate the pose of real LiDAR data with an accuracy of 1.9 m and 8.7 degrees.
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    Place questions and human-generated answers: A data analysis approach
    Hamzei, E ; Li, H ; Vasardani, M ; Baldwin, T ; Winter, S ; Tomko, M ; Kyriakidis, P ; Hadjimitsis, D ; Skarlatos, D ; Mansourian, A (Springer, Cham, 2020-01-01)
    This paper investigates place-related questions submitted to search systems and their human-generated answers. Place-based search is motivated by the need to identify places matching some criteria, to identify them in space or relative to other places, or to characterize the qualities of such places. Human place-related questions have thus far been insufficiently studied and differ strongly from typical keyword queries. They thus challenge today’s search engines providing only rudimentary geographic information retrieval support. We undertake an analysis of the patterns in place-based questions using a large-scale dataset of questions/answers, MS MARCO V2.1. The results of this study reveal patterns that can inform the design of conversational search systems and in-situ assistance systems, such as autonomous vehicles.