Abstract
Abstract. Emerging traffic management technologies, smart parking applications, together with transport researchers and urban planners are interested in fine-grained data on parking space in cities. However, there are no standardized, complete and up-to-date databases for many urban areas. Moreover, manual data collection is expensive and time-consuming. Aerial imagery of entire cities can be used to inventory not only publicly accessible and dedicated parking lots, but also roadside parking areas and those on private property. For a realistic estimation of the total parking space, the observed use of multi-functional traffic areas is taken into account by segmenting not only parking areas but also roads according to their purpose. In this paper, different U-Net based architectures are tested for detecting all these types of visible traffic areas. A new large-scale, high-quality dataset of manual annotations is used in combination with selected contextual information from OpenStreetMap (OSM) to depict the actual use as parking space. Our models achieve a good performance on parking area segmentation, and we show the significant impact of OSM data fusion in deep neural networks on the simultaneous extraction of multiple traffic areas compared to using aerial imagery alone.
Highlights
Accurate information on parking spaces is nowadays relevant for parking guidance systems as well as for traffic management and urban planning
One annotation dataset on aerial imagery and the corresponding FullyConvolutional neural Network (FCN) separates non-paved parking places (Azimi et al, 2019)
A significant category of parking areas has not been considered so far, which requires a new approach for semantic scene understanding: dual-use areas in backyards, on the roadside and on sidewalks that are regularly used for parking, no markings are visible on aerial imagery
Summary
Accurate information on parking spaces is nowadays relevant for parking guidance systems as well as for traffic management and urban planning. The relevance of parking for citywide traffic is recognized but rarely addressed (Habib et al, 2012) This is mainly caused by the insufficient data basis: the example of Germany shows that there is no standardized, up-to-date and comprehensive database even for the three largest cities (Senate of Hamburg, 2020, State capital of Munich, 2019, Senate of Berlin, 2014). The latter aspect must be emphasized, since despite a considerable share of private parking spaces, only those on public property are covered. A significant category of parking areas has not been considered so far, which requires a new approach for semantic scene understanding: dual-use areas in backyards, on the roadside and on sidewalks that are regularly used for parking, no markings are visible on aerial imagery
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