Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

In many countries, densification is a central goal of land policies to promote sustainable development. However, while these policies intervene in existing built-up structures and thus affect diver...

The implementation of sustainable land policies is in need of monitoring methods that go beyond a mere description of the proportion values of land use classes. The annual statistical surface area report on actual land utilization (German: “Bodenfläche nach Art der tatsächlichen Nutzung”), published by the statistical offices of the German federal states and the federation, provides information on a set of pre-defined land use classes for municipalities, districts and federal states. Due to its surveying method of summing up usage information from cadastral registers, it is not possible to determine previous and subsequent usages of land parcels. Hence, it is hard to precisely indicate to what extent particular land use classes contribute to the settlement area increase. Nevertheless, this information is crucial to the understanding of land use change processes, which is needed for a subsequent identification of driving forces. To overcome this lack of information, a method for the spatial and quantitative determination of previous and subsequent land usages has been developed, implemented and tested. It is based on pre-processed land use data for different time slices, which are derived from authoritative geo-topographical base data. The developed method allows for the identification of land use changes considering small geometric shifts and changes in the underlying data model, which can be adaptively excluded from the balance.

We compare different matching methods for distinguishing building modifications from replacements based on multi-temporal building footprint geometries from 3D city models. Manually referenced footprints of building changes were used to determine which thresholds are suitable for distinction. In addition, since the underlying LoD1 (Level of Detail 1) data is highly accurate, randomly generated position deviations were added to allow for transferability to less well-matched data. In order to generate a defined position deviation, a novel method was developed. This allows determination of the effects of position deviations on accuracy. Determination of these methods’ suitability for manipulation of data from sources of different levels of generalization (cross-scale matching) is therefore not the focus of this work. In detail, the methods of ‘Common Area Ratio’, ‘Common Boundary Ratio’, ‘Hausdorff Distance’ and ‘PoLiS’ (Polygon and Line Segment based metric) were compared. In addition, we developed an extended line-based procedure, which we called ‘Intersection Boundary Ratio’. This method was shown to be more robust than the previous matching methods for small position deviations. Furthermore, we addressed the question of whether a minimum function at PoLiS and Hausdorff distance is more suitable to distinguish between modification and replacement.