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Automated Detection and Vectorization of Road Elements in High Resolution Orthographic Images

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F23%3A00366659" target="_blank" >RIV/68407700:21260/23:00366659 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.5194/isprs-archives-XLVIII-5-W2-2023-111-2023" target="_blank" >https://doi.org/10.5194/isprs-archives-XLVIII-5-W2-2023-111-2023</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.5194/isprs-archives-XLVIII-5-W2-2023-111-2023" target="_blank" >10.5194/isprs-archives-XLVIII-5-W2-2023-111-2023</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automated Detection and Vectorization of Road Elements in High Resolution Orthographic Images

  • Original language description

    This paper proposes, describes, and applies an algorithm for the automatic detection of selected elements of road infrastructure, along with the option to determine their spatial information. The principle is based on the evaluation of the color spectrum of the selected object on orthographic images. As a source image used for the processing, output from low-altitude aerial photogrammetry or terrestrial laser scanning can be used, together with the option to implement digital elevation models into the processing. The approach is based on the detection of the color composition of the selected element of the road, followed by clustering of the identified elements within the image and mathematical transformation of the clusters into a spatial vector form. Prior to the processing, the target objects are filtered out based on user input, for which vectorization is performed. The outputs are in the form of contours or the determined basic structure of the object. The main difference compared to existing methods is that the vectorization is only performed on the selected, pre-filtered parts of the raster image with identified target objects, not the whole image. This approach makes it possible to effectively and automatically identify and analyze, e.g., the edge of the road, road markings, or road features. This enables the subsequent implementation of the identified outputs into more complex spatial models of the road or its proximity. Additionally, the processing of the data to create a digital model of the environment can be automated, with a significant saving of time and related costs.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20104 - Transport engineering

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    ISPRS Archives

  • ISBN

  • ISSN

    2194-9050

  • e-ISSN

    2194-9050

  • Number of pages

    6

  • Pages from-to

    111-116

  • Publisher name

    ISPRS

  • Place of publication

    Munich

  • Event location

    Almaty

  • Event date

    Jun 15, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article