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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

GI Forum [online]

ISSN

2308-1708

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

14

Strana od-do

122-135

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85122999583