Automatic Identification and Vectorization of Traffic Infrastructure Features from Orthophoto Images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F25%3A00386138" target="_blank" >RIV/68407700:21260/25:00386138 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5194/isprs-annals-X-5-W3-2025-41-2025" target="_blank" >https://doi.org/10.5194/isprs-annals-X-5-W3-2025-41-2025</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5194/isprs-annals-X-5-W3-2025-41-2025" target="_blank" >10.5194/isprs-annals-X-5-W3-2025-41-2025</a>
Alternative languages
Result language
angličtina
Original language name
Automatic Identification and Vectorization of Traffic Infrastructure Features from Orthophoto Images
Original language description
Orthophoto imaging of the Earth's surface using unmanned aerial systems have in recent years become a common and efficient method for acquiring highly detailed orthophoto maps. These are widely used in transportation and civil engineering fields. In the context of traffic accidents and technical documentation, such imagery can be applied for accurate reconstruction of the scene. However, this process often requires manual vectorization of selected road infrastructure features. This task is time-consuming and demanding, especially in more complex scenarios. The presented paper introduces a newly proposed method for semi-automatic vectorization of road infrastructure features from raster imagery. The method was implemented in MATLAB and consists of several sequential steps. These include selection of the area of interest, colour identification, noise reduction, clustering, and generation of vector contours. The entire process emphasizes simplicity, computational efficiency, and ease of use without the need for machine learning or extensive training data. Statistical evaluation using a paired t-test (p = 0.0022) confirmed that the automated approach is significantly faster than manual processing. On average, the proposed semi-automatic vectorization process was 2.15 times faster. In realistic scenarios, such as entire intersection areas, a speed increase of up to 3.1 times was achieved. These results confirm the practical benefit of the proposed method for efficient and rapid processing of traffic infrastructure image documentation.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20104 - Transport engineering
Result continuities
Project
<a href="/en/project/EH23_021%2F0009003" target="_blank" >EH23_021/0009003: SimulUK - Simulation environment in the Ústí nad Labem Region</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference Applied Photogrammetry and Remote Sensing for Environmental and Industry „APRSEI – PHEDCS 2025 Tashkent“
ISBN
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ISSN
2194-9050
e-ISSN
2194-9050
Number of pages
7
Pages from-to
41-47
Publisher name
International Society of Photogrammetry and Remote Sensing
Place of publication
Nice
Event location
Tashkent
Event date
Sep 23, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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