Analysis Ready (Meta)Data
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| Number of Parts | 351 | |
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| License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/69068 (DOI) | |
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| Production Year | 2022 |
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00:00
Mathematical analysisSatelliteSeries (mathematics)Mathematical analysisTerm (mathematics)Element (mathematics)Computer animation
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DemosceneWeb portalExponentialabbildungOpen setSatelliteCase moddingSource codeComputer fileOffice suiteSpacetimeSineMetadataPixelAngleDivisorLevel (video gaming)Data conversionSimilarity (geometry)IntegerProduct (business)Nichtlineares GleichungssystemContent (media)Spectrum (functional analysis)Scaling (geometry)Musical ensembleMultiplicationTime seriesAngleSpecial unitary groupComputing platformMultiplication signComputer animationProgram flowchart
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FreewareMathematical analysisComputing platformVirtual realitySeries (mathematics)File formatProcess (computing)MetadataAxonometric projectionSurfacePoint cloudPartial derivativePixelThresholding (image processing)Absolute valueLevel (video gaming)SatelliteState of matterInternet forumContrast (vision)Product (business)FamilyBroadcast programmingStandard deviationCollaborationismComputer programmingProcess (computing)GeometryTime seriesSatelliteLatent heatSimilarity (geometry)Cartesian coordinate systemProjective planeType theorySet (mathematics)Term (mathematics)Mathematical analysisBlogAuditory maskingPoint cloudLevel (video gaming)Multiplication signComputer animation
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MetadataMathematical analysisCartesian coordinate systemPoint cloudLevel (video gaming)Auditory maskingComputer animation
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Process (computing)outputMeasurementLevel (video gaming)PixelView (database)MetadataDifferent (Kate Ryan album)Computer animation
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MetadataFunction (mathematics)Different (Kate Ryan album)Level (video gaming)Right angleMetadataMathematical analysisComputer animation
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MetadataPoint cloudDenial-of-service attackProcess (computing)Level (video gaming)MeasurementMetadataMeasurementPoint cloudProcess (computing)Auditory maskingMathematical analysisAlgorithmLevel (video gaming)Computer animation
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MetadataMathematical analysisFile formatPoint cloudLibrary catalogComputer animation
Transcript: English(auto-generated)
00:01
Hello, everybody. My name is Matt Hanson. I'm the geospatial engineering lead at LMN-84, and today I'm going to briefly talk about analysis-ready data and what it means, and I can skip to the end and tell you that it actually doesn't mean anything at all. Everybody uses this term, ARD, and they go, oh, our data's ARD, and guess what?
00:22
There's no actual standard saying what ARD is. So, a little while ago, there was a series of ARD workshops, and I've been involved with CEOS on defining ARD, and nobody has yet to agree on it, and a lot of satellite companies say that it's ARD. So first I want to start with a cautionary tale about publicly available data.
00:43
So it turns out when you actually make data publicly available, people use it, and they use it in ways that you just didn't expect, and scientists actually don't much care for that. So here we see that once the NASA data and the ESA data was made free and available
01:01
and made available on AWS platform, everybody downloaded this data and they used it, and very few of these people, I think, actually downloaded and looked at the Landsat 8 user handbook, and if they had, they would have looked in it, and they would have saw that the data that was actually distributed wasn't corrected for the sun angle, which means
01:22
that you couldn't actually compare this data across multiple days, and yet people were taking and calculating NDVI and looking at days and time series, and I went to the Landsat science team meeting a few years ago. I was like, you know that people are using this data that way, and they're like, oh, they shouldn't do that. Well, so Landsat program started releasing ARD.
01:44
They actually coined the term the USGS and came out with this ARD project, and the idea was that it was all of the Landsat satellites, the whole constellation that could be constructed in a time series, so they were tiled in a regular projection, and they were atmospherically corrected and geo-registered, and there was a cloud mask so that you could create these
02:02
time series. Well, it wasn't long before other people started using this term ARD to kind of just mean, oh, like you can just use this data and you can compare it to other data sets, but again, the problem is that there's actually no standard associated with this. So the CEOS, which is the Committee for Earth Observation Satellites, they do have
02:20
an ARD specification. This is mostly out of Geoscience Australia, and they have all these requirements. It's pretty complicated, and you have to go through a really complex process to have your data evaluated, and a couple years later, Chris Holmes wrote this blog post about defining what analysis-ready data is, which is pretty much kind of the similar type of
02:44
thing that Landsat is, which is that, okay, this data is level two data, it's atmospherically corrected, it's been BRDF corrected, and geo-registered, and it's aligned to this common grid. At this satellite interoperability workshop, the general agreement among all the people there was that there actually was no simple definition of ARD because it was completely
03:04
dependent on the application. So I want you to think of applications such as like a cloud scientist who's looking at clouds, right, they don't need the cloud mask, or some AI applications actually don't even need level two data. So I actually would like to present an alternative view of what ARD should be, is that it's
03:25
not about having actually certain requirements on that data. It doesn't matter if it's level one or level two, or if it's been geometrically corrected to some sub-pixel accuracy, none of that matters, right? What matters is that you actually have characterized this data within the metadata.
03:41
Because the workflows all have different requirements, and workflows themselves can actually validate that metadata that they need to produce some valid output. So if a workflow can work on level one data, right, then that's kind of ARD data, right? Like that's analysis ready, because my workflow can just take that data and produce
04:00
what it needs to. And so I want people to start thinking about cloud-native geospatial workflows working on metadata and validating what it is that they need, and data producers actually providing all of the metadata there that's needed so that automatically, programmatically
04:21
algorithms can determine whether or not that data is valid for the process. And there's a lot of metadata to consider, the processing level, the geometric and measurement accuracies, calibration, and cloud masks. But as long as you characterize this, then that's really all that's needed.
04:40
So stop thinking about analysis-ready data. Start thinking about analysis-ready metadata. Thank you.