Visual approaches being qualitative are generally more neglected than statistical ones which are considered to be more objective. They are still underused for at least three reasons. Visual methods can be very important for the evaluation of spatial data and can balance some weaknesses of statistical methods.

High resolution DTMs are thus more prone to errors. Experience indicates that the effort is proportional to the square of the inverse value of horizontal resolution. This trend can also have some impact on the quality of the DTMs used if it influences usability significantly.ДЈThe higher the resolution, the more difficult the evaluation of input data quality and the assessment of the resulting DTM are. Thirdly, applications using DTMs are now part of our everyday lives (e.g., Google Earth 3, Microsoft Virtual Earth 4, NASA World Wind 5, Radrouten Planer 6вЂ¦). These additional data sources can provide valuable input for integrated DTM production, as exemplified in Slovenia (Podobnikar, 2005) and in Europe (EuroGeographics, 2008). Even datasets without height attributes such as lines of a hydrological network, roads, railways, and standing water polygons can be used (Podobnikar, 2005). In addition to the aerial photographs and contour lines, different point datasets with height attributes could also be applied, such as fundamental geodetic network points, boundary points of land-cadastre, databases of buildings, spot elevations, and other related datasets such as highway construction or hydrological network measurements. The second factor is the increasing availability of additional data sources that are useful for the DTM quality assessment or enhancement. For larger scales and more local usage, airborne laser scanning (ALS) techniques have been applied 2 (e.g. At small scales (coarser spatial resolution) radar interferometric techniques (IfSAR) had been applied to generate global DTMs 1 (Burrough and McDonnell, 1998 Maune, 2001). The first was the introduction and development of new methods for data acquisition, especially from satellites and airplanes. 2 airborne LIDAR for local DTMs with resolution of around 1 mДўThe quality of DTMs significantly increased over the last decade due to three significant factors:.NASAвЂ™s SRTM (Shuttle Radar Topography Mission) with a horizontal /planimetrical/ resolution o (.) It is suggested that applying visual methods in addition to the more objective statistical methods would result in a more efficient improvement of the quality. The fourth class gathers other possible visualisations and algorithms. The four classes generate different outputs: the first two produce thematic maps, while the third is used for non-spatial visualisation.

Four classes of visual methods are defined: visualisations according to spatial analytical operations based on one or multiple datasets visualisations according to spatial statistical analysis non-spatial visualisations and other visualisation techniques/other algorithms. In this paper, several enhanced visual techniques for quality assessment are described and illustrated with areas and datasets selected from Slovenia and the planet Mars. In contrast, visual methods are generally neglected despite their potential for improving DTM quality. Quality assessment of data is a critical parameter for DTM production and it relies heavily on statistical methods. DTMs are created by integrating data obtained from a wide range of techniques including remote sensing and land surveying. A Digital Terrain Model (DTM) is a continuous representation of a ground surface landform that is commonly used to produce topographic maps.

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