Vehicle detection using panchromatic high-resolution satellite images as a support for urban planning. Case study of Prague's centre

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F22%3A10251739" target="_blank" >RIV/61989100:27350/22:10251739 - isvavai.cz</a>

Result on the web

<a href="https://sciendo.com/article/10.2478/geosc-2022-0009" target="_blank" >https://sciendo.com/article/10.2478/geosc-2022-0009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/geosc-2022-0009" target="_blank" >10.2478/geosc-2022-0009</a>

Result language

angličtina

Original language name

Vehicle detection using panchromatic high-resolution satellite images as a support for urban planning. Case study of Prague's centre

Original language description

The optical sensors on satellites nowadays provide images covering large areas with a resolution better than 1 meter and with a frequency of more than once a week. This opens up new opportunities to utilize satellite-based information such as periodic monitoring of transport flows and parked vehicles for better transport, urban planning and decision making. Current vehicle detection methods face issues in selection of training data, utilization of augmented data, multivariate classification or complexity of the hardware. The pilot area is located in Prague in the surroundings of the Old Town Square. The WorldView3 panchromatic image with the best available spatial resolution was processed in ENVI, CATALYST Pro and ArcGIS Pro using SVM, KNN, PCA, RT and Faster R-CNN methods. Vehicle detection was relatively successful, above all in open public places with neither shade nor vegetation. The best overall performance was provided by SVM in ENVI, for which the achieved F1 score was 74%. The PCA method provided the worst results with an F1 score of 33%. The other methods achieved F1 scores ranging from 61 to 68%. Although vehicle detection using artificial intelligence on panchromatic images is more challenging than on multispectral images, it shows promising results. The following findings contribute to better design of object-based detection of vehicles in an urban environment and applications of data augmentation. (C) 2022 Sciendo. All rights reserved.

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

10500 - Earth and related environmental sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

GeoScape

ISSN

1802-1115

e-ISSN

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Volume of the periodical

16

Issue of the periodical within the volume

2

Country of publishing house

PL - POLAND

Number of pages

12

Pages from-to

108-119

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85146008780