【正文】 column=39。 b170/b1 b221/b2 b317/b3 b49/b4 b57/b5 b76/b7 /pixel pixel id=39。 row=39。 column=39。? image pixel id=39。 Classification The advent of XMLbased technologies has gone beyond the expectations of the most optimistic users. Revolutionary ideas are emerging for the storage, exchange and display of data and new formats are created for almost all kinds of data, applications and knowledge domains. A considerable number of specifications have been issued by international anizations aiming at the provision of an efficient ‘‘open’’ environment to the user munity. Watching this frenzy trend of transforming everything into XMLbased structures, one thing seems really out of the way: raster images. Languages like HTML and SVG do not have such a structural feature in their environment. Instead, both of them provide means (., img or image elements) for the incorporation of raster images in textbased files, usually with an inline reference. The existing specifications instead of dealing with the problem bypass it and users treat raster formats more or less like a requisite tool for their work. The truth is that there is nothing mon between XMLbased structures and raster images. Raster image encoding is neither text based nor human readable and it cannot be parsed, checked for validity or well formedness. Moreover, the raster image content has almost no flexibility (apart from resizing) in an XMLbased environment, since pixel values are well locked inside the raster formats (Antoniou and Tsoulos, 2021). Converting a raster formatted image into XML enables the user to utilize the information residing in the image and select, read and manipulate the parts of the XML file or the file as a whole in a number of ways in accordance with the application at hand. This is the starting point for classification, statistical processing, filtering or the development of other applications with the use of XML technology. Operations like the storage and exchange of images acquire a new meaning in the framework of an interoperable environment like WebGIS. Converting an image from raster to SVG is even more exciting. An SVGencoded image file enjoys all the abovementioned advantages。 The potential of XML encoding in geomatics converting raster images to XML and SVG Byron Antoniou, Lysandros Tsoulos Abstract The evolution of open standards and especially those pertaining to the family of XML technologies, have a considerable impact on the way the Geomatics munity addresses the acquisition, storage, analysis and display of spatial data. The most recent version of the GML specification enables the merging of vector and raster data into a single ‘‘open’’ format. The notion of ‘‘coverage’’ as described in GML can be the equivalent of a raster multiband dataset. In addition, vector data storage is also described in detail through the GML Schemas and XML itself can store the values of a raster dataset, as values of a multitable dataset. Under these circumstances an issue that must be addressed is the transformation of raster data into XML format and their subsequent visualization through SVG. The objective of this paper is to give an overview of the steps that can be followed in order to embody open standards and XML technologies in the raster domain. The last part of the work refers to a case study that suggests a step by step methodology to acplish classification, an important function in Cartography and Remote Sensing, using the XMLencoded images. 169。 in addition the user can visualize the effect of every change imposed on it. Geomatics is a sector that depends on images to such an extent that one could tell that images are the most valuable geodata sources. Feature extraction from a raster image is a very mon task for geographical anizations around the world. Through the bination of image information and SVG code instead of vector data, digitization can produce scalable vector data (., point, line, polyline, polygonetc. elements) or GML (Geography Markup Language) encoded data. Moreover, the need for visualization of the new ponents introduced by GML specification, such as Grid functions, requires the rendering of continuous data in XML (Antoniou and Tsoulos, 2021). Another area that will be influenced due to the evolution of open standards is interoperability. Provided that each satellite sensor stores data in its own format, the same applies to proprietary software, which also uses ‘‘closed’’ formats. The efficiency of remote sensing applications and interoperability can be enhanced with the use of open standards. By storing a raster dataset in an XMLencoded format, the information included in the dataset is conveyed intact along with the advantages that open standards bear. This paper elaborates on the technological environment, which can be utilized for the XML encoding of raster images and the extraction of information from the resulting datasets using open source methods. 2. Technological background . Extensible markup language—XML XML stands for Extensible Markup Language and it is a W3Cendorsed standard for document markup. XML describes a class of data objects called XML documents and partially describes the behavior of puter programs that process them. XML documents are made up of storage units called entities, which contain either parsed or unparsed data. Parsed data are made up of characters forming character data or markup. Markup encodes the description of the document’s storage layout and logical structure. Furthermore XML provides a mechanism to impose constraints on the storage layout and logical structure (Bray et al., 2021). XML is a metamarkup language implying that it enables the user to create his/her own tags according