Satellite parking: a new method for measuring parking occupancy
Keywords: parking, remote sensing, thresholding, high resolution
Abstract. Parking management plays a critical role in keeping urban spaces accessible and urban managers strive for an optimal balance between not enough and too much parking. Deciding which parking space can be liberated or needs to be extended requires detailed data on parking occupancy trends. In person inspection and in-situ sensors can provide such data but are too costly for city wide deployment. High-resolution satellite imagery is becoming more affordable, has the advantage of instantaneously collecting information from the whole city, is continuously being updated, and available for several years now to allow building a time series. Yet, identifying cars in satellite imagery is not a trivial task. We propose a method for classifying parking spot occupancy based on thresholding the reflectance range. The method requires individual parking spot data to be available and analyses each parking zone individually. We tested the method on a 0.5 metre resolution image (Pleiades satellite) that was specifically ordered for this purpose during a clear spring day in a medium-size city. The method has the advantage of not requiring extensive training data and is non-parametric. To assess accuracy, we collected ground truth data for the exact same moment as the image was ordered. The colour bands (blue, green, and red) performed equally well, while NIR seriously underperformed. We achieved a F1 score of 0.82 for all parking spots in the ground truth. The method is sensitive to tree canopy. When removing the tree obscured spots, the F1 score increased to 0.85. Tree canopy spots were automatically determined and filtered using NDVI.