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How are researchers using AI in Earth Observation workflows today?

Posted by @issueatlas · Outcome: unknown

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Originally posted by issueatlas · Updated

Early-career researcher working with ICESat-2 and other cryosphere datasets, asking how researchers are actually using AI in Earth Observation workflows in practice (not just what the tools could do). For a typical remote-sensing/climate workflow — finding datasets, understanding documentation and variables, downloading/preprocessing, harmonizing multiple products, writing analysis code, visualization and statistics, literature review — which stages consume the most time, where have tools like ChatGPT/Claude/Gemini/Copilot genuinely helped, where do they consistently fall short, and what would you automate if you could? Their own bottlenecks: identifying suitable datasets, understanding conventions, and harmonizing variables across products with different grids, projections, and resolutions (e.g. Southern Ocean sea ice work).

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issueatlas · · Reply permalink

good timing on this, i think about this a lot from the ML side.

where AI genuinely helps me: boilerplate plumbing. xarray/dask/zarr glue code, dask cluster setup, rechunking patterns, plotting. copilot-style tools are fast at that, and theyre decent at debugging tracebacks too. drafts of docs and paper summaries as well, though i treat those as first drafts.

where it falls short is everything product-specific. itll hallucinate variable names for a dataset it never saw, invent API calls, and worst of all do something like pick a fill value or a reprojection default that silently corrupts your numbers. you still have to read the actual product docs and sanity-check against the metadata yourself. no way around that yet.

the harmonizing-grids-and-projections thing you mention is my biggest time sink too, and its the part these tools help with least, because the conventions live in scattered docs the models clearly never read.

if i could automate one thing: sanity checks after preprocessing. range checks, nan maps, quick before/after plots of the regridding, so silent errors like the ones above get caught instead of me eyeballing every step.

Source: https://discourse.pangeo.io/t/how-are-reseachers-using-ai-in-earth-observation-workflows-today/5804