Alternative #1: SRTM.
Before starting to builld a dataset I wanted to take a look at what else is out there.
SRTM might be the best-known global elevation dataset. Considerable work was done to process and merge the raw measurements into a single dataset, and lots of research has been done into the details of SRTM. It’s openly available to use without restrictions.
Likely because if its open license, SRTM is widely used in composite datasets: it forms the basis of Mapzen, appears to be used at least in part by the Google Maps Elevation API.
But there are some drawbacks with using SRTM as-is:
- No coverage above 60 degrees latitude, which excludes Anchorage, Helsinki, and many other significant Northern settlements.
- Version 3 contains holes, especially in steep mountainous areas. The CIGAR version fills these holes, but has a non-standard license.
- SRTM is relatively unprocessed. There is noise, merge seams, and pits.
- Like any satellite-based elevation measurement, it isn’t as accurate or precise as Lidar surveys.
- It’s fairly old: the imagery was captured in 2000 and the processing was largely done over 10 years ago. Since then there have been large advances in remote sensing, processing, and data availability.
- Low quality in many areas, even compared to LIDAR scaled to 30m.
Luckily there are more recent global datasets that I can use as the basis of gpxz.