Hendrik Herold

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.

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

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.

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

Modeling Uncertainty for Change Analysis

Uncertainty is inherent to all data, experiments, and measurements. This holds also true for observations conducted using the most precise instrument as well as at the smallest, subatomic scale. The latter is known as the Principle of Uncertainty of quantum mechanics formulated by Werner Heisenberg (Heisenberg, 1927; for English, cf. Heisenberg, 1949, pp. 20-22). To the current state of knowledge, this fundamental physical principle defines by its nature the ultimate limit of human cognitive faculty.

Conclusions and Future Research

The concluding chapter sums up the thesis work by revisiting the research questions, by evaluating the initially stated hypothesis and summarizing the major contributions of the work. In conclusion, research perspectives are given for the various aspects discussed in this work.

Geoinformation from Digital Images

The computational acquisition of both quantitative and qualitative information from digital images is the research domain of image analysis and computer vision. Their methods are applied and constantly enhanced by a wide range of scientific disciplines, such as medical imaging, machine vision, remote sensing, material sciences, and physics, to name a few only. This chapter provides at the outset an overview of some basic principles of image analysis that are key to understand and approach the problem under investigation (cf. section 2.4).

Monitoring and Modeling Land Change

Long-term and spatially explicit monitoring and modeling are key to perceive, understand, forecast, and eventually govern complex and gradual geospatial processes such as land cover and land use change (working hypothesis one).

Evaluation and Discussion

In order to evaluate the methodology presented in the preceding chapters four and five, the proposed adaptive image analysis algorithm has been implemented as software prototype. This chapter introduces the implementation and evaluation strategy, describes the experiments conducted using real world data and concludes with a discussion of the results.

3D Reconstruction of Urban History Based on Old Maps

Digital libraries increasingly provide large amounts of scanned maps. These historical cartographic documents are considered as part of the cultural heritage. In a geographical context, however, old topographic maps are very valuable information sources for tracking land use changes over long periods of time. This chapter presents a methodology for the automated 3D building reconstruction from recent and old topographic maps. The presented methodology was developed focusing primarily on urban research, spatial planning, and a nationwide retrospective land-use monitoring. In the interdisciplinary discourse, perspectives and benefits of the method application in urban history and cultural heritage research and education have been identified and are presented here.