It seems that many different DEM resolutions (10m, 30m, 100m) could be created from a contour map (with, for instance 50m elevation changes marked), but of course some of the resulting DEMs will be wrong in one of two ways: 1. overly 'accurate', or 2. highly inaccurate.

Are there any general guidelines when trying to produce the most accurate DEM from a contour map?

  • Are you using Arc? There's a tool for this called Topo To Raster. Worth reading through to understand the limitations. resources.arcgis.com/EN/HELP/MAIN/10.2/index.html#//…
    – Tangnar
    Nov 12, 2015 at 16:38
  • That is a useful site - and yes I generally use ArcMap - but that link doesn't provide the type of general "rule of thumb" advice I was hoping was available. I'm looking for advice that is not specific to any software.
    – traggatmot
    Nov 12, 2015 at 16:49
  • Yeah I've not seen a rule of thumb. Based on that link, my conclusion is that creating an accurate DEM from a contour map is not so straight forward, so maybe output cell size is only one small concern of many. Especially for hydro applications, where you are trying to maintain drainage. Seems 'case-by-case' to me.
    – Tangnar
    Nov 12, 2015 at 17:03

1 Answer 1


Here is a wiki page and tool may offer some assistance. It isn't a rule of thumb as it is a complex method, but it may serve as a good stepping stone to a rule of thumb:

Grid size calculator. Nov 5, 2008. In Spatial Analyst wiki. Retrieved Nov 11, 2015, from http://spatial-analyst.net/wiki/index.php?title=Grid_size_calculator

This page seems to standardize methods for determining the appropriate DEM resolution based on characteristics of a given topography/contour map.

The useful tool I referred to is the GRID_CALC.xls available for download at the top of the page, "A simple step-by-step grid size calculator". In that tool there are various methods to derive a grid size recommendation, and with contour maps as the basis I think method 7, Complexity of Terrain, is ideal. That method's instructions are, "you can either make a transect study and observe every time the topography changes (inflection point) given a vertical precision or if you use contour data, you need to estimate the average spacing between the contours." The tool offers a coarsest, finest, and best grid cell size recommendation based on either of those inputs.

There is also a relevant description on that page, under Selection of cell size for geomorphometric analysis:

In this case study I will demonstrate how a grid resolution can be selected from a map of contours, i.e. a dataset consisting of lines digitized from a topo-map. Contour lines were extracted from the 1:50K topo-map, with the contour interval of 10 m and supplementary 5 m contours in areas of low relief. The total area is 13.69 km^2 and elevations range from 80 to 240 m. There were 127.6 km of contour lines in total, which means that the average spacing between the contours is 107 m. The grid resolution should be at least 53.5 meters to present the most of the mapped changes in relief. I then derived the distance from the contours map using the 5 m grid and displayed the histogram of the distances to derive the 5% probability distance. Absolutely shortest distance between the contours is 7 meters, and the 5% probability distance is 12.0 m. Finally, I can conclude that the legible resolution for this data set is within range 12.0-53.5 m. Finer resolutions than 12 m are unnecessary for the given complexity of terrain. Note that selection of the most suitable grid resolution based on the contour maps is scale dependant. For the contour lines digitized from the 1:5K topo maps, the average spacing between the lines is 26.6 m and the 5% probability distance is 1.6 m. This means that, at 1:5K scale, the recommended resolutions are between 1.6 and 13.3 m.

All of this information I'm referring to often cites the following article:

Hengl T., 2006. Finding the right pixel size. Computers and Geosciences, 32(9): 1283-1298.

That paper doesn't add much new to the above except for including equations for computing the coarsest, finest, and best resolution recommendations based on the considerations described above.

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