I am going to bother for a few mins in order to show a few things about the raster symbolizer implementation that has landed on 1.7. Ideally this should the first of a series of more posts, but I can’t guarantee on that :-).
Anyway, today I have been playing with some bathymetry data for the geoSDI project. I cannot share them since it is classified data but I have been told that I can show some snapshots :-).
Here below you have the gdalinfo on one of the datasets:
Driver: GTiff/GeoTIFF Files: DEM_Vulcano_Lipari_Salina4326.tif Size is 3718, 3600 Coordinate System is: GEOGCS[“WGS 84”, …. AUTHORITY[“EPSG”,”4326”]] Origin = (14.747192412574043,38.660954215168857) Pixel Size = (0.000090196304703,-0.000090196304703) Metadata: AREA_OR_POINT=Point Image Structure Metadata: INTERLEAVE=BAND Corner Coordinates: …. Band 1 Block=3718x1 Type=Float32, ColorInterp=Gray
As you can see we are talking about Float data. First of all I retiled it with gdal_transalte (striped tiff are pretty bad performance-wise) then I added a few overview using gdaladdo with nearest neighbor interpolation. Once this was done I just throw it at the GeoServer and here is the result for an untiled request (here a detailed request ) and for a tiled request. Quick explanation, when you set up a raster with no real raster symbolizer element in its style (like for the raster.sld that ships with GeoServer) you can get strange results in case your data is raw, like bathymetries, DEMs and the like. What we do is trying to render something useful by doing a local contrast stretch, hence a tiled request can have the checkerboard approach and an untiled request can look fuzzy since maximum and minimum values are used for the stretch. In the future we might computer approximated statistics for a layer at configuration time and use them for the subsequent renderings.
Beside this I wanted to show a nice extension we implemented for the raster symbolizer element. Check this style as well as this one (notice that to use the secondo you have to uncheck SLD validation since the extension is not recognized by OGC schemas). They look like the same but if you check the second one you’ll see that the color map element looks like this
To see you the differences, check the following:
I guess I have bothered enough.
Ciao a tutti.
PS. I found a minor bug today on the SLDParser which was not parsing the extended attribute. It is now fixed but unless you build things yourself, you won’t be able to test these things on 1.7.0. Drop me a few lines in case you want to try the differences and I’ll tell you how to fix this problem.
Quoting from the geoSDI web site,
“geoSDI is the complete solution for Spatial Data Infrastructure used by the Italian National Civil Protection Department.
The geoSDI solution is based on Open Source Technologies and was born to allow the real time geospatial data sharing between the Functional Centers of the Italian Civil Protection Department, being OGC (Open Geospatial Consortium) standard compliant, INSPIRE Directive compliant and CNIPA Repertorio Nazionali dei Dati Territoriali specifications compliant.”
The geoSDI infrastructure aims to provide a coherent National Spatial Data Infrastructure (NSDI) which is compliant with national and internal recognized standard as outlined by organizations like ISO and OGC as well as by initiatives like INSPIRE.
We are glad to announce that a core role in the geoSID inititive will be played by the GeoServer framework and that GeoSolutions SAS has been selected to provide professional support for the developments and customizations required. Stay tuned for more information on the topic.
Starting with the 1.7.0 release GeoServer comes with a pretty decent (well, at least IMHO :-) ) implementation of the SLD 1.0 RasterSymbolizer element (check here for a technical discussion on the implementation). On a side this means that we can try to style raster data as well as verctor data. On the other side, this means that if you update GeoServer from an older installation, let’s say 1.6.4 you can get into troubles with preexisting raster data since the old default raster.sld in no longer legal. Here you can find the old raster.sld, while here you can find the new one. which works for 1.7.x and successive releases. Long story short, rationale behind this is that with older geoServer releases we did not have a RasterSymbolizer implementation hence we were simply ignoring most elements of the raster.sld file. From 1.7.0 and on we have a decent RasterSymbolizer implementation, hence the old raster.sld style can no longer be used lightly since it makes assumptions on the underlying data. As a result the new default raster style is just an empty stub.
Summarising, if you are updating GeoSerber from 1.6.x or older, make sure to check to replace the raster.sld with the new onw.
Ciao a tutti.
At this point the extension is still quite young and is missing some key features like spatial indexing and support for well known binary. By trying it out and providing us with feedback you can help drive development of these features.
The extension is based on the recent re-architecture in GeoTools for database backed data stores. Code named “JDBC Next Generation”, it provides a framework for building new data store implementations quickly and robustly. Also based on this architecture are soon to come improved extensions for MySQL, Oracle, and DB2. A new extension for SpatiaLite is also in the works.
GeoServer is a open source project, developed and supported by a diverse group of people from around the world. To highlight this, OpenGeo has built a simple map showing the locations of GeoServer developers. Each developer’s location is marked with a blue point. Clicking on that point will produce a pop-up with the developer’s name and a brief bio.
This map interface is built using OpenLayers. The data points are saved in a PostGIS database and are served through GeoServer. OpenLayers accesses the data using the WFS protocol. The background image is currently the Google terrain layer, but will soon be replaced by OpenStreetMap data.
If you are a GeoServer developer and wish to be included on the map, let us know. Please include your name, a short bio, and the city, state, and country where you live. If anyone has any other comments about this map or the technology behind it, please write to us at email@example.com.