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Thanks for taking the time to write this out, great info. There are two hallmarks of an application that differentiate it from a data service: 1. Earth observ

Thanks for taking the time to write this out, great info. There are two hallmarks of an application that differentiate it from a data service: 1. Earth observation is a minority of the data that it manages and maintains 2. Users are not just presented with information, they are prompted to take action I would argue that levels L3 and L4 are probably falling into the same trap as the data feeds I described in the blog post. Do you know if USGS publishes download metrics are available for each dataset associated with Landsat, for instance? I bet if you made a ratio of time/investment to downloads, you'd find L2 outperforms all other categories. But I could be wrong; I have never seen the download data and don't know the relative levels of effort to produce each dataset they offer.

mturmon 4y ago
OK, in your hallmark #1, I suppose you're distinguishing between, say, topographic or land-cover maps that come from remote sensing and that might be useful in flood risk assessment, versus property-valuation or storm-drainage infrastructure maps that don't come from remote sensing and that might also be needed for flood risk assessment? I.e., the value is in a complete "application" solution to a given problem rather than in hoping for a "killer remote-sensing data product" that would (in theory) solve the problem? About your question, I'm on the analysis side, not the infrastructure side, so I don't know about the download metrics - they must be tracked but a quick search didn't turn up anything accessible. The download metrics aren't super-valuable because lots of people/groups do programmatic downloads -- the cost is zero. I think the L0-L4 distinctions is trying to illustrate these points: -- L0, L1 data are sensor-dependent and not really useful to solving problems -- There's a lot of value in developing calibrated data (L2+) -- Basic visual imagery often isn't calibrated so subsequent processing will necessarily be ad hoc and thus of limited value for any consequential decision-making

mturmon 4y ago

OK, in your hallmark #1, I suppose you're distinguishing between, say, topographic or land-cover maps that come from remote sensing and that might be useful in flood risk assessment, versus property-valuation or storm-drainage infrastructure maps that don't come from remote sensing and that might also be needed for flood risk assessment? I.e., the value is in a complete "application" solution to a given problem rather than in hoping for a "killer remote-sensing data product" that would (in theory) solve the problem? About your question, I'm on the analysis side, not the infrastructure side, so I don't know about the download metrics - they must be tracked but a quick search didn't turn up anything accessible. The download metrics aren't super-valuable because lots of people/groups do programmatic downloads -- the cost is zero. I think the L0-L4 distinctions is trying to illustrate these points: -- L0, L1 data are sensor-dependent and not really useful to solving problems -- There's a lot of value in developing calibrated data (L2+) -- Basic visual imagery often isn't calibrated so subsequent processing will necessarily be ad hoc and thus of limited value for any consequential decision-making