Manfred Ehlers

A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment) with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.

Facility management departments' responsibilities include monitoring and maintenance of building infrastructure, such as water, gas or electricity. Very often these tasks are completed using paper maps, which make integrated analysis of networks challenging. Ability to consider interior network structure and provide semantic and connectivity information supporting the required analysis operations are thus crucial. This paper presents an approach relying on Building Information Model (BIM) as a data source for obtaining information about interior utilities. The semantic and connectivity information of BIM is mapped onto a new model called Network for Interior Building Utilities (NIBU). NIBU is based on the semantic categorisation of utilities, and the spatial functions that have to be performed. Three scenarios (‘maintenance operation’, ‘emergency response’ and ‘inspection operation’) are developed to test the proposed approach. The model and its functions are implemented in spatial DBMS. The model is populated directly from a BIM server applying an Industrial Foundation Class (IFC) parser developed in-house. Five analysis functions are implemented to support spatial operations: trace upstream, trace downstream, find ancestors, find source and find disconnected. The investigation proves that BIM provides both the required semantics and attributes, and connectivity information that can facilitate analysis of interior utility networks. NIBU provides a simple yet flexible way to manage interior network information, which can be integrated into Digital Earth.

The main objective of this article is quality assessment of pansharpening fusion methods. Pansharpening is a fusion technique to combine a panchromatic image of high spatial resolution with multispectral image data of lower spatial resolution to obtain a high-resolution multispectral image. During this process, the significant spectral characteristics of the multispectral data should be preserved. For images acquired at the same time by the same sensor, most algorithms for pansharpening provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image (single-sensor, single-date fusion). For multi-date, multi-sensor fusion, however, these techniques can still create spatially enhanced data sets, but usually at the expense of the spectral consistency. In this study, eight different methods are compared for image fusion to show their ability to fuse multitemporal and multi-sensor image data. A series of eight multitemporal multispectral remote sensing images is fused with a panchromatic Ikonos image and a TerraSAR-X radar image as a panchromatic substitute. The fused images are visually and quantitatively analysed for spectral characteristics preservation and spatial improvement. It can not only be proven that the Ehlers fusion is superior to all other tested algorithms, it is also the only method that guarantees excellent colour preservation for all dates and sensors used in this study.

Current routing systems in GIS software mostly provide routes that allow the users to navigate between source and destination points in 2 dimensions. This paper describes the development of a web-based 3D routing system for a university campus using Open Source Software (OSS) and Open Specifications (OS). The system uses the advantages of interoperability and allows the integration and extension of different system components. A data model is described and the process of creating the data model and the migration of the data stored in dxf architectural drawings to the data model are explained. The paper also discusses the architecture and the interaction of the different prototype components such as 3D viewer, database, and programming languages. Furthermore, it describes customized tools that were developed to provide the users a simple interface to interact with the system through a standard internet browser.

This paper discusses the role of Geoinformatics as a new scientific discipline designed for handling of geospatial information. Depending on the scientific background of the people involved in shaping the emerging discipline, emphasis may be placed on different aspects of Geoinformatics. Applications and developments may address geoscientific, spatial planning, or computer science related matters. The scientific field of Geoinformatics encompasses the acquisition and storing of geospatial data, the modelling and presentation of spatial information, geoscientific analyses and spatial planning, and the development of algorithms and geospatial database systems. It is the position of the author that these tools from Geoinformatics are necessary to bridge the gap between Digital Earth models and the real world with its real-world problems (‘connecting through location’). It is, however, crucial that Geoinformatics represents a coherent integrated approach to the acquisition, storage, analysis, modeling, presentation, and dissemination of geo-processes and not a patchwork solution of unconnected fields of activity. Geoinformatics is as such not a part of Geography, Surveying, or Computer Science, but a new self-contained scientific discipline. The current paper highlights international and national trends of the discipline and presents a number of Geoinformatics initiatives. The research and teaching activities of the newly formed Institute for Geoinformatics and Remote Sensing (IGF) at the University of Osnabrueck serve as an example for these initiatives. All these developments have lead to the long overdue formation of a scientific ‘Society for Geoinformatics’ (German: Gesellschaft für Geoinformatik – GfGI) in Germany.