@article{KnoettnerRosenbaumKurzetal., author = {Kn{\"o}ttner, Julia and Rosenbaum, Dominik and Kurz, Franz and Brunn, Ansgar and Reinartz, Peter}, title = {Rule-Based Mapping of Parked Vehicles using Aerial Image Sequences}, series = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume = {IV-2/W7}, journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, doi = {doi.org/10.5194/isprs-annals-IV-2-W7-95-2019}, pages = {95 -- 101}, abstract = {Mapping of parking spaces in cities is a prerequisite for future applications in parking space management like community-based parking. Although terrestrial or vehicle based sensors will be the favorite data source for parking space mapping, airborne monitoring can play a role in building up city wide basis maps which include also parking spaces on ancillary and suburban roads. We present a novel framework for automatic city wide classification of vehicles in moving, stopped and parked using aerial image sequences and information from a road database. The time span of observation of a specific vehicle during an image sequence is usually not long enough to decide unambiguously, whether a vehicle stopped e.g. before a traffic light or is parking along the road. Thus, the workflow includes a vehicle detection and tracking method as well as a rule-based fuzzy-logic workflow for the classification of vehicles. The workflow classifies stopped and parked vehicles by including the neighbourhood of each vehicle via a Delaunay-Graph. The presented method reaches correctness values of around 86.3\%, which is demonstrated using three different aerial image sequences. The results depend on several factors like detection quality and road database accuracy.}, language = {en} }