Automatic Detection of Driving-Lane Geometry Based on Aerial Images and Existing Spatial Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F21%3A10435912" target="_blank" >RIV/00216208:11310/21:10435912 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kGBdkmPJ-X" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kGBdkmPJ-X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1553/giscience2021_02_s122" target="_blank" >10.1553/giscience2021_02_s122</a>

Result language

angličtina

Original language name

Automatic Detection of Driving-Lane Geometry Based on Aerial Images and Existing Spatial Data

Original language description

Spatial data are a key element of geographic information systems (GIS). With the growing computational power of modern GIS, the demand for accurate and up-to-date high definition (HD) spatial data grows accordingly and increases the requirements of data acquisition. To simplify and automate the process of obtaining HD road data, several methods have been created with different approaches and stages of automation. A new method combining high resolution aerial images and existing linear road data is presented in this article. The method models roads in a vector environment at the level of single driving lanes. Object-based image analysis (OBIA) is used to identify road surface markings (RSMs) in aerial images; the geometry of RSM polygons is analysed (skeletonization, neighbourhood and context analysis, pattern recognition) in order to obtain a coherent network of driving lanes. The technique is able to distinguish automatically between solid and broken lines. The method proposed was tested and proven to satisfactorily model driving lanes, including in complex situations like junctions, roundabouts or over- or underpasses.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10508 - Physical geography

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

GI Forum [online]

ISSN

2308-1708

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

2

Country of publishing house

AT - AUSTRIA

Number of pages

14

Pages from-to

122-135

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85122999583