Extracting GIS features from high resolution imagery for map production or to update or generate GIS databases will be a common task in remote sensing in the coming years. These features are often described and defined in feature catalogues such as the German ATKIS or the military Vmap catalogues.

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.

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.

In order to utilize remote sensed images effectively, a lot of image classification methods are suggested for many years. But, the accuracy of traditional methods based on pixel-based classification is not high in general. In this study, object oriented classification based on image segmentation is used to classify Landsat images. A necessary prerequisite for object oriented image classification is successful image segmentation.

This paper describes a procedure for a multitemporal interpretation of vegetation areas in Brazil using Landsat TM images. We started with a monotemporal interpretation of the first epoch of the scene by using both spectral and structural features. For interpretation of temporal changes we discretely described temporal conditions of regions, and transferred the most probable temporal changes of the given conditions as temporal knowledge into a state transition diagram, which was used for the multitemporal interpretation of the images.

The European Union (EU) grants financial aid to farmers, producing a certain kind of useful crop. In order to administrate and to control the farmers' declarations, the EU Commission asked the member states to establish an Integrated Administration and Control System (IACS) in the year 1992. During the years of experience with the system it was found, that a high percentage of declared areas were incorrect, compared to the real situation. Therefore, the process of declaration has to be improved by the establishment of a Land Parcel Identification System, preferably based on Orthophotos.

In this paper the use of an object-oriented classification approach for the identification of basic land cover types from panchromatic images is examined. Based on data recorded by KOMPSAT-1 and SPOT-5 the same classification procedure will be applied to both data sets and the results compared to a visual classification.

Zur Klassifizierung von Landbedeckungsarten wird in zunehmendem Maße auf das objektbasierte Verfahren zurückgegriffen. Neben den durch den neuartigen Klassifizierungsansatz verbundenen Vorteilen, wie beispielsweise der Segmentierung kompakter Objekte, die dem menschlichen Wahrnehmungsvermögen entsprechen, hat diese Methode auch ihre Schwachstellen. Mit der Bildung homogener Objekte geht gleichzeitig auch eine Generalisierung einher.

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.

Various regions in Europe have suffered from severe flooding over the last decennium. Earth observation techniques can contribute toward more accurate flood hazard modelling and they can be used to assess damage to residential properties, infrastructure and agricultural crops. For this study, detailed land cover maps were created by using IKONOS-2 high spatial resolution satellite imagery. The IKONOS-2 image was first divided into segments and the land cover was classified by using spectral, spatial and contextual information with an overall classification accuracy of 74%.