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This research is primarily focused on utilizing available airborne LiDAR data and spatial data from the OpenStreetMap (OSM) database to generate 3D models of buildings for a large-scale urban area. The city center of Ljubljana, Slovenia, was selected for the study area due to data availability and diversity of building shapes, heights, and functions, which presented a challenge for the automated generation of 3D models. To extract building heights, a range of data sources were utilized, including OSM attribute data, as well as georeferenced and classified point clouds and a digital elevation model (DEM) obtained from openly available LiDAR survey data of the Slovenian Environment Agency. A digital surface model (DSM) and digital terrain model (DTM) were derived from the processed LiDAR data. Building outlines and attributes were extracted from OSM and processed using QGIS. Spatial coverage of OSM data for buildings in the study area is excellent, whereas only 18% have attributes describing external appearance of the building and 6% describing roof type. LASTools software (rapidlasso GmbH, Friedrichshafener Straße 1, 82205 Gilching, GERMANY) was used to derive and assign building heights from 3D coordinates of the segmented point clouds. Various software options for procedural modeling were compared and Blender was selected due to the ability to process OSM data, availability of documentation, and low computing requirements. Using procedural modeling, a 3D model with level of detail (LOD) 1 was created fully automated. After analyzing roof types, a 3D model with LOD2 was created fully automated for 87.64% of buildings. For the remaining buildings, a comparison of procedural roof modeling and manual roof editing was performed. Finally, a visual comparison between the resulting 3D model and Google Earth's model was performed. The main objective of this study is to demonstrate the efficient modeling process using open data and free software and resulting in an enhanced accuracy of the 3D building models compared to previous LOD2 iterations. [ABSTRACT FROM AUTHOR]

• Comprehensive analysis of the LCA database of the DGNB. • Identification of qualitative flaws in the database. • Statistical analysis of 22 buildings LCAs. • Development of the basics for LCA benchmarks of buildings. • Recommendation for high-quality and uniform databases on buildings LCA. The evaluation of environmental aspects in the early planning phase of buildings can support the reduction of the resource use and environmental impacts associated with the building sector over the whole building life-cycle. The integration of life cycle assessment (LCA) benchmarks in the planning phase is one potential measure. To derive these benchmarks a large database of existing building assessment is essential. Potential data input is available from the German Sustainable Building Council (DGNB), as it certifies more than 200 buildings annually and the certification includes a mandatory LCA. In this study, the current submission files and database of the DGNB are assessed and critically reviewed with regard to their usability for automated LCA benchmarks. First, a harmonized database is created from the large number of assessed buildings. Second, the data is examined for its suitability for benchmarking with regard to data format, structure and level of detail. The data that were declared fit for purpose were used to create an exemplary, harmonized data set with 22 office buildings. The evaluation of these data for various environmental indicators of the individual life-cycle phases shows their respective relevance and can thus serve as a benchmark. Another focus is to encourage improvement of the additional documentation like the energy source required for better benchmarking, interpretation of results and auditing of the LCA rules for building certification. The results of this study highlight the opportunities and challenges in the development of a database for benchmarking. Before long-term LCA benchmarks can be developed and deployed, a standardized and uniform submission format of results, that is indifferent regarding the used LCA software, needs to be developed. In the future the submission process should be extended by an automated quality assurance to prevent restraints from low data quality and data gaps that otherwise have to be detected manually. [ABSTRACT FROM AUTHOR]

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. OPEN ACCESS [ABSTRACT FROM AUTHOR]

The residential sector is facing challenges to reduce greenhouse gas (GHG) emissions, since its heat supply is largely based on fossil fuels. One way to accelerate the reduction process is seen in expanding energy planning from single buildings to groups of buildings, forming smart neighborhoods. This study presents a heat and power system optimization for a group of ten residential buildings located in a rural settlement in Germany. Thereby, a simulation platform is interlinked with an optimization model. Demand profiles, solar potentials and options of thermal refurbishment from the simulation platform serve as input data for the optimization model that considers numerous investment options for energy storage units and demand coverage. If costs of GHG emissions are considered in the optimization, a reduction of these emissions by about 60% compared to the status quo is cost-optimal. With regard to heat supply, the cost optimum is identified in the combination of decreasing heat demand through thermal refurbishments and covering the remaining demand mainly by heat from a combined heat and power (CHP) plant in addition with a wood-fired heating system. Therefore, the model results suggest to connect seven of the ten buildings via a local heating network. The demand for electricity is partly covered by the CHP plant, partly covered by solar generated electricity and partly covered by grid consumption. [ABSTRACT FROM AUTHOR]