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  • 1
    Online Resource
    Online Resource
    Geospatial Information Research: State of the Art, Case Studies and Future Perspectives
    Springer Science and Business Media LLC ; 2022
    In:  PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Vol. 90, No. 4 ( 2022-08), p. 349-389
    Details
    In: PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Springer Science and Business Media LLC, Vol. 90, No. 4 ( 2022-08), p. 349-389
    Abstract: Geoinformationsforschung: Stand der Technik, Fallstudien und Zukunftsperspektiven. Die Geoinformationswissenschaft (GI-Wissenschaft) befasst sich mit der Entwicklung und Anwendung geodätischer und informationswissenschaftlicher Methoden zur Modellierung, Erfassung, gemeinsamen Nutzung, Verwaltung, Erkundung, Analyse, Synthese, Visualisierung und Bewertung von Daten über raum-zeitliche Phänomene im Zusammenhang mit der Erde. Als interdisziplinäre wissenschaftliche Disziplin konzentriert sie sich auf die Entwicklung und Anpassung von Informationstechnologien, um Prozesse auf der Erde und Interaktionen zwischen Mensch und Raum zu verstehen, Trends und Muster in den beobachteten Daten zu erkennen und vorherzusagen sowie die Entscheidungsfindung zu unterstützen. Die Autoren – Mitglieder der Abteilung Geoinformatik im Ausschuss für Geodäsie der Bayerischen Akademie der Wissenschaften, die die geodätische Forschung und die universitäre Lehre in Deutschland vertreten – haben diesen Beitrag erstellt, um zukünftige Forschungsfragen und -richtungen in der Geoinformationswissenschaft aufzuzeigen. Für die verschiedenen Facetten der GI-Wissenschaft wird der Stand der Technik dargestellt und mit meist eigenen Fallbeispielen untermauert. Der Aufsatz verdeutlicht damit, welche Beiträge die deutsche GI-Community leistet und welche Forschungsperspektiven sich in der GI-Wissenschaft ergeben. Darüber hinaus wird gezeigt, dass die GI-Wissenschaft mit ihrer Expertise in der Datenerfassung und -interpretation, der Informationsmodellierung und dem Informationsmanagement zur Lösung vieler der großen gesellschaftlichen Herausforderungen von heute und morgen beitragen kann.
    Type of Medium: Online Resource
    ISSN: 2512-2789 , 2512-2819
    URL: Article
    DOI: 10.1007/s41064-022-00217-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2888495-4
    detail.hit.zdb_id: 2886415-3
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  • 2
    Online Resource
    Online Resource
    Analyzing the Supply and Detecting Spatial Patterns of Urban Green Spaces via Optimization
    Springer Science and Business Media LLC ; 2019
    In:  PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Vol. 87, No. 4 ( 2019-10), p. 137-158
    Details
    In: PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Springer Science and Business Media LLC, Vol. 87, No. 4 ( 2019-10), p. 137-158
    Type of Medium: Online Resource
    ISSN: 2512-2789 , 2512-2819
    URL: Article
    DOI: 10.1007/s41064-019-00081-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2888495-4
    detail.hit.zdb_id: 2886415-3
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  • 3
    Online Resource
    Online Resource
    An Algorithmic Framework for Labeling Network Maps
    Springer Science and Business Media LLC ; 2018
    In:  Algorithmica Vol. 80, No. 5 ( 2018-5), p. 1493-1533
    Details
    In: Algorithmica, Springer Science and Business Media LLC, Vol. 80, No. 5 ( 2018-5), p. 1493-1533
    Type of Medium: Online Resource
    ISSN: 0178-4617 , 1432-0541
    URL: Article
    DOI: 10.1007/s00453-017-0350-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 1458414-1
    detail.hit.zdb_id: 53958-2
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  • 4
    Online Resource
    Online Resource
    Cover Image
    Wiley ; 2021
    In:  Transactions in GIS Vol. 25, No. 5 ( 2021-10)
    Details
    In: Transactions in GIS, Wiley, Vol. 25, No. 5 ( 2021-10)
    Type of Medium: Online Resource
    ISSN: 1361-1682 , 1467-9671
    URL: Issue
    URL: Article
    DOI: 10.1111/tgis.v25.5
    DOI: 10.1111/tgis.12868
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2035222-0
    SSG: 14
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  • 5
    Online Resource
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    Extracting spatial patterns in bicycle routes from crowdsourced data
    Wiley ; 2017
    In:  Transactions in GIS Vol. 21, No. 6 ( 2017-12), p. 1321-1340
    Details
    In: Transactions in GIS, Wiley, Vol. 21, No. 6 ( 2017-12), p. 1321-1340
    Abstract: Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning.
    Type of Medium: Online Resource
    ISSN: 1361-1682 , 1467-9671
    URL: Issue
    URL: Article
    DOI: 10.1111/tgis.2017.21.issue-6
    DOI: 10.1111/tgis.12280
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 2035222-0
    SSG: 14
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  • 6
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    An Empirical Study on Interfaces for Presenting Large Sets of Point Features in Mobile Maps
    Informa UK Limited ; 2023
    In:  The Cartographic Journal
    Details
    In: The Cartographic Journal, Informa UK Limited
    Type of Medium: Online Resource
    ISSN: 0008-7041 , 1743-2774
    URL: Article
    DOI: 10.1080/00087041.2023.2182354
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2113652-X
    SSG: 14,1
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  • 7
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    Online Resource
    BEYOND MAXIMUM INDEPENDENT SET: AN EXTENDED MODEL FOR POINT-FEATURE LABEL PLACEMENT
    Copernicus GmbH ; 2016
    In:  ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. XLI-B2 ( 2016-06-07), p. 109-114
    Details
    In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. XLI-B2 ( 2016-06-07), p. 109-114
    Abstract: Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in the map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label overlaps) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, interdependences between labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate for randomly generated instances.
    Type of Medium: Online Resource
    ISSN: 2194-9034
    URL: Article
    DOI: 10.5194/isprsarchives-XLI-B2-109-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    detail.hit.zdb_id: 2874092-0
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  • 8
    Online Resource
    Online Resource
    BEYOND MAXIMUM INDEPENDENT SET: AN EXTENDED MODEL FOR POINT-FEATURE LABEL PLACEMENT
    Copernicus GmbH ; 2016
    In:  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. XLI-B2 ( 2016-06-07), p. 109-114
    Details
    In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. XLI-B2 ( 2016-06-07), p. 109-114
    Abstract: Abstract. Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in the map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label overlaps) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, interdependences between labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate for randomly generated instances.
    Type of Medium: Online Resource
    ISSN: 2194-9034
    URL: Article
    DOI: 10.5194/isprs-archives-XLI-B2-109-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    detail.hit.zdb_id: 2874092-0
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  • 9
    Online Resource
    Online Resource
    Beyond Maximum Independent Set: An Extended Integer Programming Formulation for Point Labeling
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 11 ( 2017-11-06), p. 342-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 11 ( 2017-11-06), p. 342-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6110342
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 10
    Online Resource
    Online Resource
    Multirow Boundary-Labeling Algorithms for Panorama Images
    Association for Computing Machinery (ACM) ; 2015
    In:  ACM Transactions on Spatial Algorithms and Systems Vol. 1, No. 1 ( 2015-08-13), p. 1-30
    Details
    In: ACM Transactions on Spatial Algorithms and Systems, Association for Computing Machinery (ACM), Vol. 1, No. 1 ( 2015-08-13), p. 1-30
    Abstract: Boundary labeling deals with placing annotations for objects in an image on the boundary of that image. This problem occurs frequently in situations in which placing labels directly in the image is impossible or produces too much visual clutter. Examples are annotating maps, photos, or technical/medical illustrations. Previous algorithmic results for boundary labeling consider a single layer of labels along some or all sides of a rectangular image. If, however, the number of labels is large or the labels are too long, multiple layers of labels are needed. In this article, we study boundary labeling for panorama images, where n points in a rectangle R are to be annotated by disjoint unit-height rectangular labels placed above R in K different rows (or layers). Each point is connected to its label by a vertical leader that does not intersect any other label. We present polynomial time algorithms based on dynamic programming that either minimize the number of rows to place all n labels or maximize the number (or total weight) of labels that can be placed in K rows for a given integer K . For weighted labels, the problem is shown to be (weakly) NP-hard; in this case, we give a pseudo-polynomial algorithm to maximize the weight of the selected labels. We have implemented our algorithms; the experimental results show that solutions for realistically sized instances are computed instantaneously. We have also investigated two-sided panorama labeling, for which the labels may be placed above or below the panorama image. In this model, all of the aforementioned problems are NP-hard. For solving them, we propose mixed-integer linear program formulations.
    Type of Medium: Online Resource
    ISSN: 2374-0353 , 2374-0361
    URL: Article
    DOI: 10.1145/2794299
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2015
    detail.hit.zdb_id: 2845842-4
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