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A journey of optimization of cloud-based geospatial data processing

28 点作者 BrainBuzzer6 个月前

4 条评论

jandrewrogers6 个月前
As a meta-topic, the standard formats for geospatial are virtually all deeply suboptimal to the point of being arguably broken for modern large-scale geospatial analysis. What we have is a giant collection of narrow solutions to narrow problems, none of which efficiently interoperate with each other. It hasn’t modernized much at all, the tool chain is basically the same tech we had 25 years ago with a fresh coat of paint.<p>Fixing this is a difficult and extremely deep engineering and computer science problem, which is why there has been so little progress. Most improvements have been driven by users, but they aren’t able to make the investments to fix the problems underneath their problem, they have other work to do. Any fundamental improvements would materially break existing workflows, so adoption would be slow at best.<p>The entire spatial domain is stuck in a deep local minima.
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scottcha6 个月前
I wonder how this compares to the use of zarr format which seems to have similar goals and design and is already well integrated in several libraries (particularly xarray)?
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whinvik6 个月前
As someone who deals with geospatial processes like this daily, I have 2 notes. 1. STAC implementations are already complicated. Not everyone has good catalogues, or that work uniformly well for all types of queries. 2. Using STAC geoparquet on top and then another layer on top would mean we have to self host yet another catalog, essentially a new standard.<p>In short, even though I believe STAC adoption is what we should aim for, in reality we usually end up building workarounds.
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lmc6 个月前
Interesting ideas. If you do some subsequent post on this, it would be great to see how the perf benefits scale with different sizes of AoIs.
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