An object-based approach was used to generate a detailed land cover classification covering the Stordalen area near Abisko, northern Sweden. First, an orthophoto was combined with the DEM resampled to 1 m spatial resolution. A segmentation layer was generated by grouping pixels into homogeneous areas with a minimum region size of 130 m². From this a water mask was classified using the red band of the orthophoto and a slope layer. A land cover training set was created by combining field survey information with visual interpretation of the orthophoto and topography. The following layers were used as input for the classification algorithm: an orthophoto, elevation and slope; a SPOT5 4-band satellite image, and NDVI, SAVI and NIR/SWIR as SPOT5 derivatives. The segments were then classified using a support vector machine algorithm. Artificial surfaces were hand digitized and masked out. The individual thematic classes are described in TableA1.The quality of the classification was assessed using a set of 108 ground control points. The accuracy assessment results in a Kappa value of 0.71 and an overall accuracy of 74% for all land covers excluding water and artificial areas see TableA2.