Slashgeo

Geospatial Solutions offers a long and informative article named bringing JPEG 2000 into the GeoWeb. From the article: "JP2 uses wavelet-based algorithms to efficiently store an image at multiple resolutions, removing the need for users to preprocess imagery into “pyramids.” JP2 also uses advanced arithmetic encoding techniques to provide significantly greater image quality than JPEG [...] In addition to high image quality, JP2 supports multiple bands (for multi- or hyperspectral datasets) and signed data and allows for 16 or more bits of precision. These are all features that GIS workflows might require. The JP2 file format also supports storage of more than one image per file, as well as rich colorspace models, user-defined metadata, and more."

jive writes "The GeoTools 2.3.0 release is available for download: * http://geotools.codehaus.org/2.3.0 This release brings together a lot of great improvements made over the last year. The main focus of this release is improving the raster story for GeoTools. While plug-ins for a variety of rasters existed previously, all of them were memory constrained and not really suitable for real software. 2.3.0 brings very solid, scalable, fast support for ArcGrid, GeoTiff, GTOPO30, World Images and Image Mosaics and Pyramids. These are already in action in GeoServer 1.5.x, and will soon work their way in to uDig." The rest of the Press Release below.

st_0x0ef writes "From Berkaoui blog : "[...] Tiling and speed have always been issues with Internet Mapping — especially with Raster Images. Just recently Klokan Petr Pridal described his Summer of Code project as being able "to allow easy publishing of raster maps on the Internet. Your raster file (like TIFF/GeoTIFF, MrSID, ECW, JPEG2000, JPEG, PNG) is converted into a directory structure of small tiles ( TMS compatible ), which you can just copy to the webserver. Simple webpages with viewers based on Google Maps and OpenLayers are generated as well — so anybody can comfortably explore your maps on-line and you do not need to install or configure any special software (like mapserver) and the map displays very fast in the webbrowser. [...]"" See also this informative OSGeo wikipage on the project.

st_0x0ef writes "Understanding how an image file can be represented in a small compressed file and how it may be reconstructed it is very helpfull if we want to work efficiently with large dataset. In this article, we will see how a JPEG file represents an image using a fraction of the computer storage that might be expected. We will also look at some of the mathematics behind the newer JPEG 2000 standard.

For those who lack the strong mathematical foundation that is required to understand the stuff from this article, I will try to describe what each format does in more accessible terms. JPEG tries to find blocks with very similar colours and will make one block of this. The compression is obtained because for example instead of having to describe an 8x8 block (which means you have to state 64 times which color the tiny 1x1 block is), you can simply say that the entire block is one colour. JPEG is so successful, because the human eye can only distinguish a limited number of shades of colour, whereas a computer can store quite a lot that we are never able to distinguish." The rest of st_0x0ef's submitted review below, see also the link above for full article with images.

The Lin.ear th.inking blog ran an entry last month on spherical indexing schemes for PostGIS. Thought not exhaustive, the entry is still interesting and reminds us how spatial indexes are important to efficiently analyze huge spatial databases. From the entry: "Handling geodetic data in a correct and efficient way presents quite a few challenges. A major one is: how can geodetic geometry be spatially indexed? Conventional spatial indexes (such as 2D R-trees) all rely on geometry being embedded in a planar space. They don't handle data which can "wrap around", as can occur in a spherical space." See below for other (rare) stories related to spatial indexes.