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.

Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. A systematic relationship is found between the road length and type and the parking area obtained. It is suggested that these results are informative to those needing information on parking in structurally similar regions.

Abstract. Highly accurate reference vehicle trajectories are required in the automotive domain e. g. for testing mobile GNSS devices. Common methods used to determine reference trajectories are based on the same working principles as the device under test and suffer from the same underlying error problems. In this paper, a new method to generate reference vehicle trajectories in real-world situations using simultaneously acquired aerial imagery from a helicopter is presented. This method requires independent height information which is coming from a LIDAR DTM and the relative height of the GNSS device. The reference trajectory is then derived by forward intersection of the vehicle position in each image with the DTM. In this context, the influence of all relevant error sources were analysed, like the error from the LIDAR DTM, from the sensor latency, from the semi-automatic matching of the vehicle marking, and from the image orientation. Results show that the presented method provides a tool for creating reference trajectories that is independent of the GNSS reception at the vehicle. Moreover, it can be demonstrated that the proposed method reaches an accuracy level of 10 cm, which is defined as necessary for certification and validation of automotive GNSS devices.

<p>The transport sector accounts for about one quarter of worldwide anthropogenic carbon dioxide emissions. Since a robust growth in transport activity is expected over the coming decades, reducing associated emissions to mitigate human-caused climate change is a particular challenge. In order to achieve high-quality comparative monitoring, to develop scenarios for future emissions, and to enable a robust assessment of climate protection measures, the allocation of emissions to the subsector level is a necessary prerequisite. The DLR project ELK – EmissionsLandKarte (en.: emission map) contributes here in several respects: (1) requirements are specified in an application-based manner, i.e. compatibility with existing inventories, such as the ones generated for IPCC, is ensured and insufficiencies in spatial resolution and methodological detail are addressed, (2) an input database congruent with both statistical data and SSP scenarios is provided, and (3) bottom-up calculations are performed that allow attribution of climate impacts to specific transport services, as well as prospective analyses where, for example, activity levels change or alternative fuels affect regional emission factors. The resulting prototype global gas and particle emission inventories for land transport, aviation and shipping reflect the status quo as of 2019.</p><p>For land transport, fine-grained activity and vehicle fleet data as well as technology-specific emission factors are applied. This allows emissions from passenger and freight transport to be disaggregated by mode and vehicle type. New approaches for spatial disaggregation of emissions will increase transparency of the methodology. For aviation, calculations are based on fleet composition and transport performance for both passenger and cargo traffic at the airport pair level, while real flight tracks serve as the foundation for spatial allocation. For both transport sectors, complementary analyses are performed to characterize particulate emissions in order to fill gaps in data availability. For shipping, transport performance on inland waterways and maritime routes are considered, including technical data describing propulsion and bunkering. Finally, all mode-specific results are subjected to an innovative uncertainty assessment aligned with the needs of other emission inventory creators through a detailed evaluation per uncertainty factor, as well as aggregated values for climate modelers and practitioners. The consistent assessment of uncertainty factors along the entire calculation chain, such as activity levels, emission factors, and proxy data used for spatial or temporal disaggregation, promotes comparability across all transport sectors. In this paper, we outline the new methodological approaches for mapping transport emissions and present first results.</p>

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