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

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.

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

“…Following this, deep learning methods like SVM, KNN, PCA, RT, and Faster R-CNN have gained prominence in traffic flow analysis. Golej et al [21] utilized these techniques alongside high-resolution satellite imagery for vehicle detection.…”

“…Uses machine learning, including SVM, KNN, PCA, RT, and Faster R-CNN, for vehicle detection. [21] The ANST model combines LSTM and attention mechanisms for traffic forecasting. [24] Using spatial context mining and a support vector machine model to identify transport modes from big data.…”

Satellite parking: a new method for measuring parking occupancy

“…Several studies tried to detect cars from satellite imagery. Starting in the late 2000's researchers have used classification techniques to detect cars in satellite imagery (Sharma et al, 2006;Eikvil et al, 2009), while in recent years machine learning based approaches have become popular (Golej et al, 2022;Cao et al, 2016). These studies developed methods to detect all cars in a complete image and did not specifically focus on parked or stationary cars.…”