New data resources from high spatial resolution satellite sensors like Ikonos or Quickbird, and innovative concepts in image analysis have the potential for improving mapping and analysis of urban land use structures and related dynamics. Considering the high amount of spatial detail in those types of data and the land cover heterogeneity of urban areas it is more difficult to apply traditional digital image analysis algorithms to derive thematic information.

Die Entwicklung der Formen und Phänomene der Erdoberfläche - sowohl die natürliche als auch die vom Menschen gestaltete Landschaft - lässt sich mit Luftbildern übersichtlich und bestechend detailliert nachvolhziehen. 

The study sets out the benefits of the new IKONOS satellite image data for urban planning and for urban information tasks in general. Following a brief delineation of the reference modalities,the quality of the data is assessed and the problems for georeferencing are pointed up.Diverse fields of application of the data in urban planning and aministration are then enumerated.

Detecting urban features from high resolution remote sensing data such as IKONOS might become one of the most challenging tasks of remote sensing within the coming years. In order to carry out urban planning and development tasks adequate spatial data basis' are needed. In many cases this can only be obtained through the analysis of remote sensing data. Especially IKONOS as a speceborne sensor offers a suitable resolution combined with an easy to handle acquisition.

Monitoring urban land use requires very high spatial resolution image data, acquired either by airborne or spaceborne sensor systems. The information content of such images is very complex, thus information extraction is currently performed on the basis of visual interpretation. The aim of the research described in this study is to formalise the visual interpretation procedure in order to automate the land use mapping process for urban areas.

Pixel-based classifications have difficulty adequately or conveniently exploiting expert knowledge or contextual information. Object-based image-processing techniques overcome these difficulties by first segmenting the image into meaningful multipixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels. The segments (objects) are assigned classes using fuzzy logic and a hierarchical decision key. To date, the main drawback has been the lack of effective software.

Multi-sensor remote sensing systems have been developed for a simultaneous acquisition of image and elevation data. However, reliable automatical processing methods – in particular for the interpretation of multi-sensoral data with high geometrical resolutions – are still at a development stage and by no means operational yet. In this context it will be shown how the increased potential of multi-sensor systems can be used to extract and process additional features for the purpose of urban landscape modelling.

Traditional methods use NDVI to investigate vegetation cover from remote sensing imagery. These methods provide per-pixel vegetation distribution, and cause a modifiable areal unit problem (MAUP), when a meaningful statistical result is issued. In this paper, a new method based on advanced segmentation techniques and classification is proposed for urban vegetation investigation extraction. This method utilizes ASTER data to build a hierarchical multi-resolution structure, so as to reflecting the inherent relationship between ground features under various scale levels.

For establishing sealed area mapping in urban areas, the role of NDVI is crucial but not completely understood. Normally a linear relationship is assumed. The limits of this linear model are tested and the restrictions analysed. For the various tests a hierarchical approach is chosen. Starting with the central role of texture analysis for city footprint extraction, the resulted mask is used to differentiate between sealed and unsealed areas within the city border.

This paper describes the development of techniques for the production of urban mapping data from interferometric polarimetric synthetic aperture radar (SAR) data.