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Ergebnisse nach Hochschule und Institut
Publikation Real-time generalization of point data in mobile and web mapping using quadtrees(Taylor & Francis, 29.04.2013) Bereuter, Pia; Weibel, RobertWith a focus on mobile and web mapping, we propose several algorithms for on-the-fly generalization of point data, such as points of interest (POIs) or large point collections. In order to achieve real-time performance, we use a quadtree data structure. With their hierarchical subdivision structure and progressive levels of detail, indices of the quadtree family lend themselves as auxiliary data structures to support algorithms for generalization operations, including selection, simplification, aggregation, and displacement of point data. The spatial index can further be used to generate several local and global measures that can then serve to make educated guesses on the density and proximity of points across map scales, and thus enable control of the operation of the generalization algorithms. An implementation of the proposed algorithms has shown that, and thanks to the quadtree index, real-time performance can be achieved even for large point sets. Furthermore, the quadtree data structure can be extended into a caching structure, which can be used to store pre-computed generalizations; thus, a desired level of detail (LOD) can simply be retrieved from cache.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Content zooming and information exploration for web and mobile maps. Adaptation of real-time map generalisation to the information seeking strategies of web and mobile users(Lovoisier, 2013) Bereuter, Pia; Weibel, Robert; Burchardt, DirkIn the context of the development of mobile map applications with capabilities for map generalisation and abstraction, we propose a methodology for content exploration that uses a technique that we term content zooming to change the degree of abstraction of map content independently of the map scale. Content zooming allows overriding the effects of ‘standard’ map generalisation, focusing on optimised content representation to aid the information seeking task of a mobile user. It is thus complementary to map generalisation. The paper defines content zooming operations and proposes technical solutions for each of these, illustrating them with examples from a research prototype.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Generalisation operators(Springer, 08.07.2014) Stanislawski, Lawrence V.; Buttenfield, Barbara P.; Bereuter, Pia; Savino, Sandro; Brewer, Cynthia A.; Burghardt, Dirk; Duchêne, Cécile; Mackaness, WilliamThis chapter summarises cartographic generalisation operators used to generalise geospatial data. It includes a review of recent approaches that have been tested or implemented to generalise networks, points, or groups. Emphasis is placed on recent advances that permit additional flexibility to tailor generalisation processing in particular geographic contexts, and to permit more advanced types of reasoning about spatial conflicts, preservation of specific feature characteristics, and local variations in geometry, content and enriched attribution. Rather than an exhaustive review of generalisation operators, the chapter devotes more attention to operators associated with network generalisation, which illustrates well the logic behind map generalisation developments. Three case studies demonstrate the application of operators to road thinning, to river network and braid pruning, and to hierarchical point elimination. The chapter closes with some summary comments and future directions.04A - Beitrag SammelbandPublikation Editorial(Taylor & Francis, 23.01.2020) Touya, Guillaume; Bereuter, Pia; Raposo, Paulo01A - Beitrag in wissenschaftlicher ZeitschriftItem Outdoor mobile mapping and AI-based 3D object detection with low-cost RGB-D cameras. The use case of on-street parking statistics(MDPI, 05.08.2021) Nebiker, Stephan; Meyer, Jonas; Blaser, Stefan; Ammann, Manuela ; Rhyner, SeverinA successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.01A - Beitrag in wissenschaftlicher Zeitschrift