Dear Edzer and Alexander STAC is rapidly expanding to become a “de facto” standard. STAC is being adopted by relevant cloud data providers of Earth observation data, such as Microsoft Planetary Computing and Earth on AWS. Its use is gradually replacing the OCG catalogue specification.
The “rstac” package is able to access STAC endpoints. For examples on how to use the “rstac” package directly, there is a nice tutorial by Marius Appel (author of the “gdalcubes” package) that shows how to combine “rstac” with “gdalcubes”. It’s available at https://youtu.be/Xlg__2PeTXM?t=3693 For R examples of access to large cloud computing collections using STAC under the hood, please take a look at the following chapter of the “sits” package documentation: https://e-sensing.github.io/sitsbook/earth-observation-data-cubes.html Hope this helps. Best Gilberto ============================ Prof Dr Gilberto Camara Senior Researcher National Institute for Space Research (INPE), Brazil https://gilbertocamara.org/ ============================= > On 7 Oct 2022, at 16:13, Edzer Pebesma <edzer.pebe...@uni-muenster.de> wrote: > > STAC is clearly the future of catalogues for spatial data, but not everyone > has gotten there yet. Searching or browsing available STACs is helped by stac > index, https://stacindex.org/ > > On 07/10/2022 18:43, Zivan Karaman wrote: >> Hi, >> Perhaps STAC <https://stacspec.org/en/> could help you? >> Best, >> Zivan >> On Fri, Oct 7, 2022 at 6:35 PM Alexander Ilich <ail...@mail.usf.edu> wrote: >>> >>> Hi, I was wondering if anyone has some advice on how to organize raster >>> data so that it is easily queryable by various attributes (e.g. find me all >>> the rasters of data type bathymetry, collected by this organization with >>> 10m resolution or finer ). Currently we have data on a server organized >>> often by when/where it was collected but that can make it difficult to find >>> specific rasters that meet a certain criteria. I've created a table as a >>> csv file on github <https://github.com/ailich/WFS_Multibeam_Metadata> where >>> each row is a raster and it has various column attributes describing it >>> (e.g. who collected it, what sonar was used, resolution, coordinate system, >>> etc) and a path to the filename as a temporary solution, but I think some >>> type of spatial database that would allow for querying and then reading >>> into R as terra objects, as well as into QGIS and ArcGIS as layers for >>> visualization would be optimal as multiple project members use these data. >>> Tools I've come across that seem potentially useful include PostGIS and >>> Geopackage, but I'm not entirely sure how to properly set them up or if >>> they'd suit my needs. Any advice would be greatly appreciated. >>> >>> Thanks, >>> Alex >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> R-sig-Geo mailing list >>> R-sig-Geo@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >> _______________________________________________ >> R-sig-Geo mailing list >> R-sig-Geo@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo > > -- > Edzer Pebesma > Institute for Geoinformatics > Heisenbergstrasse 2, 48151 Muenster, Germany > Phone: +49 251 8333081 > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
