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<p>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.</p>
</abstract>

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.

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

Department of Geoinformatics, Faculty of Mining and Geology

GeoScape

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Data publikacji: 05 sty 2023

Zakres stron: 108 - 119

Otrzymano: 28 wrz 2022

Przyjęty: 02 gru 2022

DOI: https://doi.org/10.2478/geosc-2022-0009

Słowa kluczowe
Vehicle detection, WorldView, Segmentation, Supervised classification, Neural network, Faster R-CNN

© 2022 Peter Golej et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Data publikacji: 05 sty 2023

Zakres stron: 108 - 119

Otrzymano: 28 wrz 2022

Przyjęty: 02 gru 2022

DOI: https://doi.org/10.2478/geosc-2022-0009

Słowa kluczoweVehicle detection, WorldView, Segmentation, Supervised classification, Neural network, Faster R-CNN

© 2022 Peter Golej et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Vehicle detection, WorldView, Segmentation, Supervised classification, Neural network, Faster R-CNN