Testing location data with edge cases, not just happy paths
An address in a well mapped city center tells you almost nothing about how your system handles a rural route, a disputed border, or a query near the poles. Test the hard cases deliberately.
How elevation data sources get validated
Validating elevation data works on a similar principle to validating geocoding accuracy: you need points where the true answer is already independently known, and you compare the data against those known points rather than trusting it on faith. For elevation specifically, this means checking returned values against fixed survey benchmarks, points whose height above sea level has been independently and carefully measured through traditional surveying methods, and treating any meaningful, systematic deviation from those known values as a signal worth investigating.
A useful validation exercise samples benchmark points spread across different terrain types, flat ground, moderate slopes, and steep terrain, rather than only checking easy, flat locations where nearly any reasonable elevation data source will perform well and agree closely with the known value. As covered elsewhere, terrain type strongly affects how much elevation accuracy can be expected at a given resolution, so a validation exercise limited to flat benchmark points will systematically overstate how well the data performs on steeper or more complex terrain elsewhere.
A second, complementary check that does not require a known survey point at all is internal consistency across a cluster of nearby samples. Querying elevation at several points a short, known distance apart and checking whether the resulting values change smoothly and plausibly, rather than jumping erratically in ways inconsistent with the real terrain, is a useful sanity check on its own, since real terrain does not typically change in random, uncorrelated jumps between adjacent points that are actually close together. Sudden discontinuities between very nearby samples, absent a genuine sharp terrain feature like a cliff edge or a building, are a reasonable signal to double-check the data or the specific area rather than trust it outright.
A third useful check, where feasible, is cross-referencing against a second independent elevation source for the same set of points and looking specifically at where the two sources agree closely versus where they diverge meaningfully. Close agreement across most points, with occasional divergence concentrated in known-difficult terrain like steep slopes or dense urban canyons, is exactly the pattern you would expect from two reasonably good sources measuring a genuinely hard problem, and it is a healthier sign than suspiciously perfect agreement everywhere, which can sometimes indicate two sources drawing from the same underlying data rather than genuinely independent measurement.
If elevation accuracy matters meaningfully for your use case, running this kind of validation yourself against known benchmarks relevant to your specific region and terrain type will tell you far more than any general statement about elevation data quality. Our elevation lookup accepts a list of points in a single request, which makes it straightforward to run exactly this kind of benchmark and consistency check efficiently across a meaningful sample.