Which pixels to select when subset does not match pixel boundaries #6
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This existing OGC document may have material you can reuse: http://docs.opengeospatial.org/is/10-140r2/10-140r2.html <http://docs.opengeospatial.org/is/10-140r2/10-140r2.html>
… On Aug 7, 2020, at 9:09 AM, jratike80 ***@***.***> wrote:
Imagine to have some raster data with pixel size 10 by 10 units and bbox starting at 0,0. Let's put the anchor point at the centre of the pixel. Now user requests a subset as
&SUBSET=E(572,612)&SUBSET=N(477,518)
<https://user-images.githubusercontent.com/1751612/89645168-ecb82400-d8c1-11ea-9a72-d7712e2478a0.png>
Which pixels should be selected?
A) All pixels that intersect with the subset
<https://user-images.githubusercontent.com/1751612/89646239-14a88700-d8c4-11ea-9ca7-afae44e1e75a.png>
B) Only pixels which all totally contained by the subset
<https://user-images.githubusercontent.com/1751612/89646370-53d6d800-d8c4-11ea-9553-6577270e9c07.png>
C) Only pixels whose anchor points are within the subset
<https://user-images.githubusercontent.com/1751612/89646455-79fc7800-d8c4-11ea-82aa-6cfc525de111.png>
With plain GetCoverage no resampling nor scaling should happen and if I understand right, in all three cases the envelope of the result is different from the envelope of the subsetting request. Also, if user sends a new request with subset envelope that is adjacent to the previous one, interpretation A would give duplicate pixels, interpretation B) would lead to rows of missing pixels, and only alternative C) would give a resultset that is adjacent to the first resultset.
These sections in the WCS 2.0 Core deal somehow with the case but I feel that they do not answer my question.
The WCS Core standard defines
the domain subsetting operation which delivers all data from a coverage inside a specified
request envelope (“bounding box”), relative to the coverage’s envelope – more precisely, the
intersection of the request envelope with the coverage envelope.
Requirement 32 /req/core/getCoverage-request-trim-within-extent:
Let the extent of the coverage’s gml:Envelope along the dimension specified in the trim
request range from L to H. Then, for the trim bounds trimLow and trimHigh the following
shall hold: L <= trimLow <= trimHigh <= H.
Let further
c be the OfferedCoverage of the server addressed;
low = tLow if specified in the request, otherwise low is set to the coverage’s lower
bound in dimension dname;
high = tHigh if specified in the request, otherwise high is set to the coverage’s upper
bound in dimension dname;
B be an envelope equal to the domain of c, except that in dimension dname the extent
is given by the closed interval [low,high];
Then, the following requirement holds:
Requirement 38 /req/core/getCoverage-response-trimming:
The response to a successful GetCoverage request on coverage identifier id of admissible
type containing no slicing and exactly one trimming operation with dimension name dname,
lower bound parameter evaluating to low, and upper bound parameter evaluating to high
shall be a coverage identical to c, but containing all points of c with location inside B, and
no other points.
NOTE This requirement does not specify the actual extent of the coverage returned. Possible options include: the minimal bounding box of the coverage returned, or the request bounding box. Servers are strongly encouraged to deliver the minimal bounding box.
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I believe the correct answer is C. At least that's the way CubeWerx has implemented it. The way I see it, coverages typically deal with points, not pixels. When a coverage is returned in a pixel-based representation such as GeoTIFF, it represents each sample point by a pixel, where the sample point is at the centre of each pixel. If a sample point intersects the requested subset, it should be returned in the GeoTIFF response as a pixel; otherwise it shouldn't. CubeWerx's implementation of the various OGC web services distinguishes between what we call a "cell-based" extent (used, for example, by WMS) and a "grid-based" extent (used, for example, by WCS). A grid-based extent is always one pixel smaller in each dimension (0.5 pixels per side) than its corresponding cell-based extent. So if there's a sample point at each integral unit and a coverage subset of E(572,612) N(477,518) is requested, the resulting GeoTIFF will contain 41x42 pixels (representing 41x42 sample points). Its grid-based extent is exactly what was requested (572,477 to 612,518) but its cell-based extent (that is, corner to corner) is 571.5,476.5 to 612.5,518.5. It you wanted to make a map request of this coverage subset, that'd be the extent you'd specify. If a coverage subset of E(571.9,612.1) N(476.9,518.1) is requested, you'd get exactly the same response. However, if a coverage subset of E(572.1,611.9) N(477.1,517.9) is requested, you'd get a response that's only 39x40 pixels in size, representing the subset E(573,611) N(478,517). It would have a grid-based extent of 573,478 to 611,517 and a cell-based extent of 572.5,478.5 to 610.5,517.5. |
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@pomakis I am not very familiar with CIS, but aren't both pixel-based and cell-based valid coverages? GeoTIFF for example supports both PixelIsArea and PixelIsPoint, as described here.
Does WCS really imply values represent point, and does not support values representing areas? |
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AFAIK this is a specialty of GeoTIFF (cf CIS GeoTIFF encoding) and not supported by GRIB, NetCDF, etc. |
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re subsetting behavior, here how rasdaman does it: https://doc.rasdaman.org/05_geo-services-guide.html#subsetting-behavior (I'd guess that all tools do it the same way). |
We deliver orthophotos and DEM through WCS and I believe that majority of our users believe that they are dealing with pixels. They may be wrong with DEM but anyway, they consider WCS as a more flexible alternative for downloading GeoTIFFs because they are not tied to fixed size 6 x 6 km tiles. |
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@jratike80 I would argue that both "Value is for point" and "Value is for area" are perfectly valid use cases for a coverage of data values... Based on what it is they are representing / how those values were obtained. It would be nice if OGC API - Coverages could better address this. |
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The behaviour implemented by CubeWerx is based on the Web Coverage Service Implementation Standard, version 1.1.2 (OGC 07-067r5), section 7.6.3, which states:
If the more recent standards are more flexible than this and can support either model, then this certainly needs to be made more clear (especially in the way it affects how subset extents are to be interpreted). |
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@pomakis Agreed.
If the client (or the maps renderer) does not know whether a value represents a sample collected exactly at that position, or an average, then it cannot know how to interpret or render the data and greatly contribute to further reduce the quality of the data (lossy operation), and this is a much worst problem than this being an average (which might be intended, and disagree with the point about this being a poor approximation). |
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@pomakis Ha, that dates back to Arliss Whiteside who had very clear opinions about remote sensing. What would you phrase it like instead? Would we want to discuss it in the Coverages.SWG? (need to get used to the new name!) |
jratike80
changed the title
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@pebau , I'm not a coverages expert, so I really don't know how it aught to be worded. I only know that it aught to be spelled out clearly in "OGC API - Coverages". If individual coverages are allowed to be either value-is-point or value-is-area, it aught to be clear to the client how to determine this, and it aught to be clear to the client how that affects the interpretation of the subsetting parameters. And if possible, "OGC API - Coverages" aught to be compatible with WCS in this respect so that the same subsetting request issued to either service will result in the same samples being selected. |
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"Value is for point" and "Value is for area" are properties of the coverage. Unless the API knows how to get this information from the coverage, it cannot pass it on to a client. Perhaps we need to specify a minimum set of coverage metadata for the collection information. Understanding that nil values may occur (see gml:nilReason). |
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@pomakis @pvretano An interesting fact is right now the Daraa DTED shows as |
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I agree with @cmheazel, that this is a property of the coverage. In the OGC® GML Application Schema - Coverages - GeoTIFF Coverage Encoding Profile we tried to make this explicit, e.g. with:
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with libGeoTIFF. |
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From GML 3.2.2 "When a grid point is used to represent a sample space (e.g. image pixel), the grid point represents the center of the sample space (see ISO 19123:2005, 8.2.2)." Note that the extract references ISO 19123 - Geographic information — Schema for coverage geometry and functions |
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To add my perspective to the confusion, it seems common practice for ‘Pixel is Point’/’Pixel is Area’ to be combined with the grid definition to give a grid with offsets. This ‘simplification’ strikes me as unnecessarily complicated. I think grid definition and pixel/grid-point representativeness are orthogonal and any combination could allowed. This is certainly the practice in NetCDF and GRIB formats used in meteorology. E.g. either define the grid (1-D for simplicity) to be, say, [0, 1, 2, 3, …] then the ‘Pixel is Point’ represents data at [0, 1, 2, 3, …] and ‘Pixel is Area’ could represent data at [(-½ to +½), (+½ to 1½), (1½ to 2½), (2½ to 3½), … ] Of course, one could define a grid to be [½, 1½, 2½, 3½, … ] then the ‘Pixel is Point’ represents data at [½, 1½, 2½, 3½, … ] and ‘Pixel is Area’ could represent data at [(0 to 1), (1 to 2), (2 to 3), …] I suspect the fundamental ISO19123 definition allows both options but is also misinterpreted. Which is the most convenient approach to present to a user, or the most convenient for a service to implement? |
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@chris-little In my opinion it is fundamentally wrong to move the geo-referenced position of sample data, whether they represent an area (average, minimum, maximum or otherwise) or a point by an offset based on what it is. Searching through CIS 1.1, the only reference I see to "offset vector" is this sentence:
And from the UML diagram of GeneralGrid, it describes exactly the simplistic model I was discussing today (no origin or direction or offset vector):
So I am also of the opinion that the "pixel is area" or "pixel is point" is entirely orthogonal, or worded otherwise (and to support n-dimensions, e.g. voxel is volume), along each axis, the "span" in units of measurement (or fraction of the resolution) to which the data values correspond. |
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Chris Little wrote:
[...]
I think grid definition and pixel/grid-point representativeness are
orthogonal and any combination could allowed. This is certainly the
practice in NetCDF and GRIB formats used in meteorology.
E.g. either define the grid (1-D for simplicity) to be, say, [0, 1, 2,
3, …] then the ‘Pixel is Point’ represents data at [0, 1, 2, 3, …] and
‘Pixel is Area’ represents data at [(-½ to +½), (+½ to 1½), (1½ to 2½),
(2½ to 3½), … ]
My question at this point is which way bounding boxes are interpreted.
In this example, would the lower coordinate of the bounding box of the
coverage be -½ or 0? And what about the bounding box of the map layer
for the coverage? Are they the same, or is the bounding box of the map
layer larger by half a pixel on each side (because the map world is
always PixelIsArea)?
…--
Keith Pomakis <pomakis@cubewerx.com>
Senior Software Developer, CubeWerx Inc.
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@pomakis I would understand both Bounding Boxes / Subset to always be specifying exactly the same geographic extent. For example, for a PixelIsPoint DEM, requesting exactly its geographic extent/envelope (where the corners match precisely to the corner elevation samples), returns you all points. The exact same subset or bounding box coordinates for a map of that DEM, also returns you a full map of that DEM, but a map renderer to fill that "area" between 4 samples would then shade the area in between the 4 DEM samples with an interpolated gradient between those 4 points). If the coverage is PixelIsArea satellite imagery, requesting exactly its geographic extent/envelope (where the corners match precisely to the corner elevation samples), returns you all 'pixels'. The exact same subset or bounding box coordinates for a map of that imagery, also returns you a full map of that imagery (i.e. exactly the same thing if the resolution is the same). |
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Okay, so suppose you have a PixelIsPoint DEM that represents 4x4 samples in a Lat/Lon grid where the points are at Latitudes 20 to 23 and Longitudes 40 to 43. You're saying that the bounding box for this coverage would be (20,40) to (23,43) and that making a coverage request for bbox=20,40,23,43 will return a 4x4 grid containing all 16 points. And that makes sense. But if you wanted a map of this coverage at its native resolution (one sample per pixel, so 4x4 pixels where each pixel represents exactly one sample), you couldn't do it by making a map request for bbox=20,40,23,43. Map bounding boxes are always interpreted as representing the outside edges of the pixels, so you'd get back a 3x3 map where each pixel represents the average of the four samples at its corners. The only way I can see around this is to advertise the bounding box of the map to be (19.5,39.5) to (23.5,43.5). Am I missing something? |
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@pomakis Glad we agree that the DEM coverage request makes sense :) Short answer for rendering a map of that 4x4 PixelIsPoint DEM: the native resolution to render a map out of it is NOT 4x4, it's 3x3, because a map pixel is inherently an average of the area, and those grid sample values are not averages. Detailed explanation below :) Assuming your map renderer is capable of interpolating between the original sample values, my approach is essentially identical to your (19.5,39.5)-(23.5,43.5) bbox, except for the map advertising the same extent as the coverage, but it is more accurate, as that 0.5 offset is stretching it beyond the geospatial extent actually covered by the data, since the values are spot values and not an average. It is much more convenient as well, considering that maps and coverage now share the same collection description document where the extent/envelope is specified. Now let's consider the use case of rendering a map from a DEM, and let's consider both a PixelIsArea DEM (average height) and a PixelIsPoint DEM (theoretically precise height at the sample position). For the PixelIsPoint DEM, (20,40) is the bottom-left corner of the DEM for which you have an exact value, and you have a 4x4 grid of values, so the GeneralGrid index bounds are (0,0)-(3,3). For the PixelIsArea DEM, (20,40) is still the bottom-left corner of the DEM, but the value that you have is valid for a whole cell -- so that DEM grid is 3x3, and the GeneralGrid index bounds are (0,0)-(2,2). Now let's render maps, with same bbox or subset request of the whole extent which is the same in both cases: Lat(20:23),Lon(40:43), and let's first consider rendering a map with a lot more pixels than cells in the coverage,-- i.e. still using the native resolution DEM cells values (not interpolating those), but rendering hillshading and an elevation color map with smooth interpolation. For the PixelIsPoint DEM, you will want to use the heights for exactly where they were measured, or the maps will be off, and multiple tiles rendered the same way will show a seam -- and you can interpolate the hillshading and color map in between a fair bit: For the PixelIsArea, if rendering a larger map, you may want to assume it represents the center position of a cell to be able to calculate hillshading and interpolate colors etc. Now about retrieving a map of the coverage native resolution... In the PixelIsArea DEM with a 3x3 cells grid, the map pixels can directly represent those values (same as if the map was imagery, also representing the average color of an area). So in both cases, you would get a map with 3x3 pixels, of the exact same area. No pesky 0.5 offset involved anywhere in any of these scenarios! :) (Except perhaps the maps renderer turning the 4x4 PixelIsPoint DEM into a 3x3 map which will multiply the sample values by 0.5 as it averages between the 4 corner values, but that's just a special case of 'rendering' the coverage at that resolution -- you would use different factors if you were generating more than a single pixel in between those corners). Knowing that a value represents an area or a (theoretically) infinitely small point is much more useful than an offset, which still doesn't carry that information. I really believe CIS needs to be extended to add this notion, so we can stop messing up the geo-referencing of coverages, without actually solving the fundamental problem of needing to know whether a value spans the grid resolution, or indicates a value at a precise point on the grid. @chris-little could confirm but I believe this logic can be applied not only to elevation values, but just as well for retrieving values or rendering maps of values where samples can represent anything else, at least for scenarios mapping to precise location or area of a cell (regardless of the meaning of the value: average, minimum, maximum as well, I believe...). |
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@Schpidi Do you know what those GMLCOV constructs you mentioned, e.g. gml:boundedBy, gml:offsetVector, gml:lowerCorner, gml:upperCorner, gml:origin map to in CIS 1.1? I see a mention in passing of "global offset vector", and "gml:boundedBy", but those notes seem to be floating in the air, and those elements are not defined in the CIS GeneralGrid class. I really like the simplicity of this GeneralGrid class (which corresponds directly to the DomainSet), and as I undertand it there should not be any offset in there based on the raster type. But I do think we need to add this information about the span along axes of a grid value (either simply "point or area", or as a fraction of the resolution where 0 = point .. 1 = area), in a 1.2 update to CIS (while also tackling the other important CIS issues: https://github.com/opengeospatial/ogcapi-coverages/issues/104 and https://github.com/opengeospatial/ogcapi-coverages/issues/102). |
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I would like to remind that my original question was about how to select pixels when the bounding box limits do not match with the pixel limits (or with the centre points). See the images there at the top. The context is core WCS/OGC API Coverages without resampling. But naturally there must be a consensus about the special case with pixel aligned bboxes first. |
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@jratike80 my opinion on how this should work: Regardless of whether the data is PixelIsPoint or PixelIsArea, if you do a subset for the GeneralGrid lower & upper bounds / the envelope (assuming those are the same, with no 0.5 offsets between these as discussed above), in both cases you would get all cells by passing that exact extent. Would that make sense? |
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@jerstlouis It does make sense but the PixelIsArea case would have a side effect: If user makes requests with adjacent bounding boxes, then the pixels from the boundary that is common for the both BBOXes and which are only intersected by the bounding box, but are not completely within, would be included in both adjacent result sets. In this context pixels behave like they were rectangular polygons and user makes ST_Intersects query with another geometry. It may be the best and most logical way to handle the case but it should be defined and documented. Users who are building mosaics from the individually downloaded pieces of the coverage should be aware that duplicated rows of pixels may exist at the seams of the tiles and avoid some workflows in further processing. For example more exotic blend modes https://en.wikipedia.org/wiki/Blend_modes than "normal" might lead to surprising results. With pure GetCoverage / ?Items requests the duplicated pixels in the result sets should be perfectly aligned with the same pixel values and user can simply select one or another, or the average, and the result is always the same as in the source data. If any resampling or processing is involved then the overlapping pixels are not necessarily equal. |
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a few more issues, just to underline the complexity of the topic:
This may motivate why I am quite hesitant about quick shots. |
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@jerstlouis As @pebau says above, the 'cell areas' may change across the grid. The only difficult issue is when the boundary cannot be physically extended such as north of the North Pole on a Lat/long grid. And that begs the question why did someone choose such a grid with a value 'representativeness' that extends into imaginary space? |
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To summarise my position:
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@jerstlouis I agree offsets are pesky. I certainly did not intend that the words 'vector' or 'offset' implied data would be moved. |
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I know that in the end we need to figure all of this out for the general case, but I'd like to get a handle on the simple grid case first (because if we can't get that straight then we're in trouble). So lets get back to the example of a DEM that represents 4x4 samples in a Lat/Lon grid where the points are at Latitudes 20 to 23 and Longitudes 40 to 43. Let's say the 8-bit sample values are: And let's say that this coverage has a corresponding map layer whose style is simply a grayscale (0..255) representation of the (possibly interpolated) value. @jerstlouis, if I understand you correctly, you're saying this: If the DEM is PixelIsPoint, the advertised BBOX (full extent) of DEM would be: the response of full-extent coverage request at native resolution would be: the advertised BBOX (full extent) of corresponding map layer would be: and the response of full-extent map-layer request at native resolution would be: This would mean it's impossible to get a one-sample-per-pixel map layer of the entire coverage. In fact, for the client to request a one-sample-per-pixel map layer of even a subset of the coverage, the calculations for what bounding box to request would have to include a half-pixel expansion on each edge. Now lets move on to the PixelIsArea case. You're saying that if the DEM is PixelIsArea, the the advertised BBOX (full extent) of DEM would still be: the response of full-extent coverage request at native resolution would still be: the advertised BBOX (full extent) of corresponding map layer would still be: and the response of full-extent map-layer request at native resolution would still be: Is this right? So there's no difference at all between the PixelIsPoint and the PixelIsArea cases? And in neither case is it possible to get a one-sample-per-pixel map layer of the entire coverage? I dunno, maybe I shouldn't be stuck on trying to allow the retrieval of a one-sample-per-pixel map layer of an entire simple-grid coverage. But it just seems to me that this is what the client would expect back when making a collections/{coverageId}/map?width={coverageNSamplesWide}&height={coverageNSamplesHigh} request. (In fact, the above request would return a weirder result still, since it's requesting a 4x4 interpolation of a 3x3 space.) So you can see that I'm still confused about all of this. Forgive me if I'm being dense, but I figure that if I'm struggling with this, others probably are too. |
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@jerstlouis @pomakis The coverage responses are what I would expect, as a non-expert. I do not know why any mapping software has to turn 4x4 meaningful data points into something misleadingly 3x3 with the data smoothed away to uselessness. :-{ i accept that is what some software might do. Does that matter for a coverage API? |
I think you may have misunderstood what I tried to say in one of my statements earlier... @pomakis For example, to render a 256x256 map tile of a PixelIsPoint elevation coverage, you would need to sample 257x257 elevation values, and average 4 of them to generate each pixel. You also need those same 257x257 points to generate a 3D mesh of the terrain on which to drape that map, and those 256x256 area pixels is what will look correct when draped onto that terrain. But you could render a 4096x4096 map of the same area with a much more gradual gradient between the data values (with good interpolation you can get a nice looking map with a much coarser coverage). The right-most of those 257 source elevation values horizontally, is exactly the same value as the first value of the tile to its right. |
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@pomakis In your example with byte values, you got the first (PixelIsPoint) case correct. However, that's not what I intended to say for PixelIsArea. So yes, you would still get the exact same value with a coverage subset. However, what I was saying is in this PixelIsArea case, the full extent map request would return you exactly the same 4x4 values as the coverage request: because the map pixels are also an area that correspond directly to your coverage. And I will argue that in both cases, you are getting back a one-(area)sample-per-pixel map layer of the entire coverage. |
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@jratike80 I understand your concerns about the adjacent bounding box requests, which is why I was suggesting to avoid the area limit, in order to be able to have clean non-duplicating adjacent requests of the raw coverage data:
The case where the bounding box request cuts through the middle of an area however cannot really be handled easily, which is why it could be documented that in order to avoid any duplication, requests should be made at intervals which are a multiple of the resolution, e.g. However sometimes duplication is actually desired (e.g. in our GNOSIS Map Tiles we use a 257x257 grid for coverages, which allows you to generate a triangular mesh of the whole area without depending on the adjacent tiles). So this behavior actually makes a lot of sense to me: you get all the data within the extent -- that's what |
I still struggle to understand irregular grids, so focusing on regular grids for now :) We can see whether and how this applies to irregular grids after we can agree on how to handle regular grids.
I see no reason why this logic couldn't apply equally well to instant time vs. average over a period of time (or other dimensions).
I find the area vs. point clearer than talking about 'corners' -- there's no 'corner' if we're talking about a value representing a single point? (See also my comments on GeoPackage Tiled Gridded extension grid-value-is-corner vs. grid-value-is-center at opengeospatial/ogcapi-coverages#92 (comment) -- it's an offset in disguise, with no additional information about the span of the measurement)
We are not making this assumption in terms of interpolating an existing coverage which has precise point values (I was talking about super-smooth interpolation in examples above). |
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I have prepared this presentation to better illustrate my suggestions and concerns: An interesting observation on Slide 5 is that:
although I wonder if in practice current CIS definitions and WCS services would be compatible with this approach to determine the span? |
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SWG 2021-04-21: Motion to adopt a clarification on how to determine the information whether coverage cells represent a point or area (or overlapping cells with a gap) for CIS 1.1 GeneralGridCoverage by comparing the lower and upper bound of the CRS axis against the lower and upper bound of the matching index (gridspace) axis. We will also discuss how the traditional GMLCov coverage definition of CIS 1.0 maps to the GeneralGridCoverage, and to what extent this point vs. area distinction can be transposed to that form, and which cases can be expressed losslessly as a GeneralGridCoverage (likely not possible in the opposite direction, to confirm). This information and guidance as examples is important for users to fully grasp what can be expected from a CIS 1.1 document. Motion: Jerome |
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Regarding the above note on GMLCOV / CIS 1.0, it provides no mechanism to distinguish between ValueIsPoint or ValueIsArea. However, the current GeoTIFF Coverage Encoding Profile (OGC 12-100r1) discusses the topic, since GeoTIFF does (also clarifying that lack of distinction by GMLCOV):
This means that for the same number of data cells along one axis, the The main problems with this half-pixel approach for ValueIsArea are:
Therefore I suggest that a new GML GeoTIFF encoding profile should support GeneralGridCoverage (not currently one of the supported coverage types), and allow for carrying the distinction directly in the DomainSet which would always correspond to the Envelope regardless of whether values represent points or areas, without relying on the presence of that envelope. Representing the ValueIsArea / ValueIsPoint distinction in legacy CIS 1.0 / GMLCOV can still be done by encoding and comparing the In summary, the clarification about distinct DomainSets for spatial / temporal axes for ValueIsArea vs. ValueIsPoint should apply only to the new CIS 1.1 GeneralGridCoverage domainsets, and not to those inherited from CIS 1.0 such as |
hm, what is "GML GeoTIFF"? Friendly amendment: enhance the GeoTIFF Coverage encoding spec, maybe by adding a new conformance class. @Schpidi has written the GeoTIFF extension originally, so he will know best how to update. |
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Thanks @jerstlouis for the thorough analysis. I agree with your assessment and suggestions. Whom would we need to ask to initialize a GitHub repo with the current version in Asciidoc to start working on an update? |
By "the new GML GeoTIFF encoding profile" I meant the successor to the current OGC® GML Application Schema - Coverages - GeoTIFF Coverage Encoding Profile.
As I suggested before, perhaps this could be defined by specifying GeoTIFF tags encoding the CIS 1.1 GeneralGrid DomainSet/RangeType information directly inside the GeoTIFF (and making a better association with TIFF bands for temporal axes/range value fields), instead of relying on a separate GML files, and it could then be called something like "OGC GeoTIFF CIS 1.1 Profile" or something similar. @Schpidi @gbuehler might be able to help us to set up an ASCIIDoc-populated GitHub repo for this new GeoTIFF coverage specification. |
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ok, looking forward to an "upgrade" of "CIS GeoTIFF"! Should we include GeoTIFF activists in the task force? |
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@pebau Exciting! For sure :) |
…Subsetting - This attempts to provide clarity related to opengeospatial#92 and opengeospatial/coverage-implementation-schema#6 grounded in the well-defined Dimensionally Extended 9-Intersection Model (DE9-IM). - It explicitly avoids the ambiguous concepts of "pixel offset" and "center vs. corner" which do not make obvious that the values of a gridded coverage reflect a particular cell geometry. - The cell geometry of a gridded coverage is most often defined as an infinitely small point (span = 0) or an area whose side is as large as the resolution (span = 1).
Imagine to have some raster data with pixel size 10 by 10 units and bbox starting at 0,0. Let's put the anchor point at the centre of the pixel. Now user requests a subset as
&SUBSET=E(572,612)&SUBSET=N(477,518)Which pixels should be selected?
A) All pixels that intersect with the subset
B) Only pixels which all totally contained by the subset
C) Only pixels whose anchor points are within the subset
With plain GetCoverage no resampling nor scaling should happen and if I understand right, in all three cases the envelope of the result is different from the envelope of the subsetting request. Also, if user sends a new request with subset envelope that is adjacent to the previous one, interpretation A would give duplicate pixels, interpretation B) would lead to rows of missing pixels, and only alternative C) would give a resultset that is adjacent to the first resultset.
These sections in the WCS 2.0 Core deal somehow with the case but I feel that they do not answer my question.
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