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Border Detection for Seamless Connection of Historical Cadastral Maps

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43963770" target="_blank" >RIV/49777513:23520/21:43963770 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007/978-3-030-86198-8_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-86198-8_4</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-86198-8_4" target="_blank" >10.1007/978-3-030-86198-8_4</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Border Detection for Seamless Connection of Historical Cadastral Maps

  • Original language description

    This paper presents a set of methods for detection of important features in historical cadastral maps. The goal is to allow a seamless connection of the maps based on such features. The connection is very important so that the maps can be presented online and utilized easily. We concentrate on the detection of cadastre borders and important points lying on them. Neighboring map sheets are connected according to the common border. The tasks are solved using a combination of fully convolutional networks and conservative computer vision techniques. The presented approaches are evaluated on a newly created dataset containing manually annotated ground-truths. The dataset is freely available for research purposes.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Document Analysis and Recognition – ICDAR 2021 Workshops

  • ISBN

    978-3-030-86197-1

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    16

  • Pages from-to

    43-58

  • Publisher name

    Springer International Publishing

  • Place of publication

    Cham

  • Event location

    Lausanne, Švýcarsko

  • Event date

    Sep 5, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000711902100004