As a consequence of the wide-spread application of digital geo-data in Geoinformation Systems (GIS), quality control has become increasingly important. A high degree of automation is required in order to make quality control efficient enough for practical application. In order to achieve this goal we have designed and implemented a semi-automatic technique for the verification of cropland and grassland GIS objects using 1 m pan-sharpened multispectral IKONOS imagery. The approach compares the GIS objects and compares them with data derived from high resolution remote sensing imagery using image analysis techniques. Textural, structural, and spectral features are assessed in a classification based on Support Vector Machines (SVM) in order to check whether a cropland or grassland object in the GIS is correct or not. The approach is explained in detail, and an evaluation is presented using reference data. Both the potential and the limitations of the system are discussed.