Due to financial or administrative constraints, access to official spatial base data is currently limited to a small subset of all potential users in the field of spatial planning and research. This increases the usefulness of Volunteered Geographic Information (VGI), in particular OpenStreetMap (OSM), as supplementary datasets or, in some cases, alternative sources of primary data. In contrast to the OSM street network, which has already been thoroughly investigated and found to be practically complete in many areas, the degree of completeness of OSM data on buildings is still unclear. In this paper we describe methods to analyze building completeness and apply these to various test areas in Germany. Official data from national mapping and cadastral agencies is used as a basis for comparison. The results show that unit-based completeness measurements (e.g., total number or area of buildings) are highly sensitive to disparities in modeling between official data and VGI. Therefore, we recommend object-based methods to study the completeness of OSM building footprint data. An analysis from November 2011 in Germany indicated a completeness of 25% in the federal states of North Rhine-Westphalia and 15% in Saxony. Although further analyses from 2012 confirm that data completeness in Saxony has risen to 23%, the rate of new data input was slowing in the year 2012.

Data, maps and services of the national mapping and cadastral agencies contain geometric information on buildings, particularly building footprints. However, building type information is often not included. In this paper, we propose a data-driven approach for automatic classification of building footprints that make use of pattern recognition and machine learning techniques. Using a Random Forest Classifier the suitability of five different data sources (e.g. topographic raster maps, cadastral databases or digital landscape models) is investigated with respect to the achieved accuracies. The results of this study show that building footprints obtained from topographic databases such as digital landscape models, cadastral databases or 3D city models can be classified with an accuracy of 90–95%. When classifying building footprints on the basis of topographic maps the accuracy is considerably lower (as of 76–88%). The automatic classification of building footprints provides an important contribution to the acquisition of new small-scale indicators on settlement structure, such as building density, floor space ratio or dwelling/population densities. In addition to its importance for urban research and planning, the results are also relevant for cartographic disciplines, such as map generalization, automated mapping and geovisualization.

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.

Estimating urban green volume is getting more and more important within the frame of an ecologically orientated city planning and environmentally sustainable development. The first and the last pulse of airborne Light Detection and Ranging (LiDAR) data provide the basis for the estimation of green volume, but these optimal data are not always available, particularly for urban areas. That is why this paper deals with the question whether LiDAR data (last pulse only) that have not been taken during the vegetation period allow a sufficient estimation of the green volume. This paper sets up on previous results where LiDAR data have been compared to photogrammetrically determined vegetation height measurements. The subtraction of the laser-based Digital Terrain Model and Digital Surface Model in vegetated areas leads to a vast underestimation of green volume of up to 85%, which is mainly due to the standing deciduous trees with an underestimation of 90%. Starting from the existence of different laser response characteristics of various vegetation types, the relative point density and the normalized height of classified nonground points were analyzed in depth. The results show a good separation of different vegetation types. Furthermore, a pragmatic approach of reconstruction of the underestimated vegetation (mainly deciduous trees) is carried out by generating cylinders for the classified nonground points to compensate the volume loss. The point density of nonground points and the normalized height of the laser responses were used to regulate the adaptive cylinder construction based on fuzzy logic techniques. Using reference data, the accuracy could be estimated. In spite of the suboptimal LiDAR data, this paper leads to a sufficiently exact and efficient estimation of green volume compared to the costly conventional methods like field investigations. The method makes a contribution in the field of data improvement and is applicable to similar LiDAR data of other areas.