Mapping Road Traffic Conditions Using High Resolution Satellite Images

Construction, development and maintenance of the road network are central activities for several public authorities. In cooperation with the Norwegian road authorities, we have developed an approach for automated vehicle detection and generation of traffic statistics from QuickBird images. Satellite surveillance serves several obvious advantages over the methods that are being used today, which consist of expensive single-point measurements made from pressure sensors, video surveillance etc., in or close to the road. Based on advice from the road authorities of Norway, we have selected a set of study sites from different parts of the country, such that our image data represents the diversity of road types and solar illumination conditions. Road and vegetation masks are applied to the image so that the search for vehicles is restricted to the (paved parts of the) roads only. For segmentation, we have applied techniques that seek to locate the modes of the image histogram. The resulting segments are then examined by feature extraction and classified adopting the maximum likelihood method. Additionally, we propose a new approach for car shadow removal. The described methods are implemented and tested against manual vehicle counts. We also compare the results to area traffic cover statistics estimated from single-point measurements. Manual vehicle counts indicate that there is some ambiguity in the interpretation of the images. Nevertheless, the automatic method that we have developed in this study performs very well compared with the reported manual counts. * Corresponding author.