Gotthard Meinel

Assessing image segmentation quality – concepts, methods and application

Image segmentation is a crucial step within the remote sensing information retrieval chain. As a step prior classification the quality of the segmentation result is of fundamental significance. This contribution gives an overview of existing methods for the evaluation of image segmentation quality. Furthermore, seven recent programs for remote sensing imagery are introduced and their results based on very high resolution IKONOS data are evaluated using an empirical discrepancy method.

Estimation of Urban Green Volume Based on Single-Pulse LiDAR Data

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.

Proposal of indicators regarding the provision and accessibility of green spaces for assessing the ecosystem service “recreation in the city” in Germany

ABSTRACT The paper summarises the multiple benefits of urban green spaces for city dwellers and provides an overview of proximity approaches and common key parameters for green-space quantification in cities. We propose indicators for the assessment of the ecosystem service ‘recreation in the city’ on a national scale. The calculation procedure, which takes into account the best available data sets in Germany, is explained. The determination of threshold values regarding green-space standards comprising type, size and distance is crucial to such studies. The results, the degree of provision with public green spaces in all German cities with more than 50,000 inhabitants (n = 182) and their accessibility, are presented. In total, green spaces are accessible for daily recreation for 74.3% of the inhabitants in German cities, which means that underprovision affects 8.1 million city dwellers. Some indicator details are shown for the examples of Wiesbaden and Stuttgart. Finally, we discuss the approach and values of the proposed and quantified indicators in a German and European context. EDITED BY Christine Fürst

Land-use monitoring by topographic data analysis

The rate of land consumption is an important factor to be considered within policies on sustainable land use aiming to reduce demand for settlement and traffic areas. As an expression of the political intent to achieve sustainable development, the German government announced to reduce the consumption of open space for settlement or transportation infrastructure significantly by 2020. Progress toward such specific goals can, of course, only be monitored if planning authorities are supplied with up-to-date and precise information on land use. This article presents one approach to the calculation of trends in land use that uses geoprocessing of topographic base data. Among the advantages of this approach are the nationwide availability of data with homogeneous quality and regular mandatory updating by surveying authorities. The spatial analysis of topographic base data is currently a highly automated process, which means geoprocessing procedures can be repeated regularly in order to realize time series. Such systematic monitoring of land use is undertaken by the project Monitor of Settlement and Open Space Development (IOER Monitor). By mid-2013, the fourth time period based on data from 2012 will be available online, ensuring that information on a wide range of land-use types is provided for a time series beginning back in 2006. Thus, the IOER Monitor is a convenient tool for the analysis and monitoring of land use for all administrative units ranging from German municipalities (approximately 12,000 in number) to the country as a whole, as well as for raster cells ranging from 100 m to 10 km.

Automatic identification of building types based on topographic databases – a comparison of different data sources

ABSTRACT 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.

Automatic delineation of built-up area at urban block level from topographic maps

Abstract To comprehensively study and better understand urban dynamic processes — such as densification, growth and sprawl, or shrinkage — spatio-temporal databases that allow to track changes of geographic objects like buildings and urban blocks are essential. While comprehensive databases exist for contemporary data, they usually lack a historic dimension. The manual constitution of historic geographic data, be it based on historic maps or aerial images, is a time consuming and laborious process, however. Therefore, we present an approach to semi-automatically extract this data from binary topographic maps with regard to built-up areas at urban block level. The suitability of topographic maps for historic urban analysis has been proven in previous research. To overcome the challenges that are inherent in scanned topographic maps in regard to digital image interpretation we designed a modular process. Among others, these challenges include fused and (multi-)fragmented map objects caused by the overlap of competing content layers in one single binary map. After a preliminary separation of individual map object layers from the map content, the process follows a two-stage top-down approach. At first, the map is organized into street blocks, which after that are re-delineated in regard to built-up area. In doing so, we achieve correctness values ranging from 0.97 to 0.93 for three study sites in Germany. With an increasing number of projects that provide historic topographic maps as georeferenced digital data, our process represents a promising approach to efficiently prepare these historic data for integration into a spatio-temporal database with minimal user intervention.

Using Raster DTM for Dike Modelling

Digital Terrain Models are necessary for the simulation of flood events. Therefore they have to be available for creating flood risk maps. River embankments for flood protection have been in use for centuries. Although they are artificial structures that actually do not belong to the natural elements of the land surface they are usually implicitly embedded in digital terrain data. Being elongated and elevated objects, they appear — depending on the used colour ramp for visualisation — as bright stripes on the surrounding background.

Towards a Metadata-driven Multi-community Research Data Management Service

Nowadays, the daily work of many research communities is characterized by an increasing amount and complexity of data. This makes it increasingly difficult to manage, access and utilize to ultimately gain scientific insights based on it. At the same time, domain scientists want to focus on their science instead of IT. The solution is research data management in order to store data in a structured way to enable easy discovery for future reference. An integral part is the use of metadata. With it, data becomes accessible by its content instead of only its name and location. The use of metadata shall be as automatic and seamless as possible in order to foster a high usability. Here we present the architecture and initial steps of the MASi project with its aim to build a comprehensive research data management service. First, it extends the existing KIT Data Manager framework by a generic programming interface and by a generic graphical web interface. Advanced additional features includes the integration of provenance metadata and persistent identifiers. The MASi service aims at being easily adaptable for arbitrary communities with limited effort. The requirements for the initial use cases within geography, chemistry and digital humanities are elucidated. The MASi research data management service is currently being built up to satisfy these complex and varying requirements in an efficient way. Keywords—Metadata, Communities, Research Data Management

Measuring Land Take: Usability of National Topographic Databases as Input for Land Use Change Analysis: A Case Study from Germany

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: “Bodenflache nach Art der tatsachlichen 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.

High Resolution Analysis of Settlement Structure on Base of Topographic Raster Maps --- Method and Implementation

The paper describes a method for full automatic calculation of settlement structure on base of simple topographic raster maps. In a first step extracted all buildings, they are mixed with streets, scripts and signatures in the maps. After vectorization we calculated building variables such as area, length, width, shape complexity and distance to next building. On base of this description we classified all buildings and realized a statistical analysis on building block level. We estimated 16 indicators (e. g. building and inhabitant density), visualized indicators in a GIS in optimized pre-defined legends and calculated a statistical report for the study area in different spatial resolution. The full automatic procedures called SettlementAnalyzer (SEMENTA®) have been implemented in ArcGIS under additional using of the image processing software HALCON.