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Title: ANALYSIS OF MACHINE LEARNING ALGORITHMS PERFORMANCES FOR ROAD SEGMENTATION ON VERY HIGH-RESOLUTION SATELLITE IMAGERY AS SUPPORT OF ROAD INFRASTRUCTURE ASSESSMENT
ANALYSIS OF MACHINE LEARNING ALGORITHMS PERFORMANCES FOR ROAD SEGMENTATION ON VERY HIGH-RESOLUTION SATELLITE IMAGERY AS SUPPORT OF ROAD INFRASTRUCTURE ASSESSMENT

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Ivan Brkic; Mario Miler; Marko Sevrovic; Damir Medak
10.5593/sgem2023/2.1/s08.16
10.5593/sgem2023/2.1
1314-2704
978-619-7603-57-6
English
23
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Geoinformatics
Road traffic fatalities are a significant concern worldwide, as highlighted by data from the World Health Organization (WHO) and other international organizations. One approach to enhancing road safety is through the assessment of specific characteristics or features that contribute to the overall safety condition of roads. The International Road Safety Assessment Program (iRAP) identifies several attributes that have a direct impact on road safety. Some of these attributes can be collected from satellite imagery. One of first steps in using satellite imagery as source for road attributes collection is road extraction. Quality road extraction can provide a quality base for detection of road attributes. In this paper Random forests, Extreme Gradient Boosting and U-net algorithms were analyzed to get insight into which one is most suitable for road extraction. Analysis was performed on very high-resolution satellite imagery with four spectral bands and spatial resolution of 0.3m. Analysis has shown that U-net outperformed Random forests and XGBoost in each of evaluation measures and it is suggested as best option for road extraction as support of road infrastructure assessment process.
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Authors would like to express very great appreciation to © Airbus DS (2022) for providing Pleiades Neo satellite imagery for this research.
conference
https://doi.org/10.5593/sgem2023/2.1/s08.16
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Proceedings of 23rd International Multidisciplinary Scientific GeoConference SGEM 2023
23rd International Multidisciplinary Scientific GeoConference SGEM 2023, 03 - 09 July, 2023
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.
121-128
03 - 09 July, 2023
website
9096
machine learning, satellite imagery, Random forests, XGBoost, U-net