Urban Land Use Classes with Fuzzy Membership and Classification Based on Integration of Remote Sensing and Gis

Urban land use classification from remotely sensed images has drawn great attention in the past decades. Most researchers derive land use data from remotely sensed images alone, but the results are not quite satisfying for detecting detailed land use classes in urban areas. Fuzzy urban land use classes proposed here consist of a number of fuzzy memberships that offer direct links to findings from remote sensed images and GIS data. When we compare those indicators derived from remote sensed images and GIS data with the fuzzy class memberships using fuzzy criteria or rules, we would be able to evaluate the possibilities of fuzzy membership functions of an area with respect to predefined classes. The indicators will be developed for the case that such a classification should be based on the combination of remotely sensed images and GIS data. Rules and parameters will be presented for classification with the related uncertainty levels. The final result of such a classification process will consist of a land use map and a corresponding map indicating uncertainty levels of the assignment of area to the relevant classes. The proposed approach will be based on estimating the real cover of an area by features like green space, water body, built up area etc. From these cover compositions the major functions will be inferred in a land use map.