【正文】 es of each feature are assigned to the location of the feature. The second method describes the spatiotemporal domain set and the set of values from the range set. It also describes the rule that assigns a value from the range set to each position within the domain (Cox et al., 2021). Two schemas, and support coverages in GML and the values that can be assigned in the domain set are Grid and RectifiedGrid as shown in Figs. 4 and 5. The gml:Grid element defines an nonrectified grid, which is a work posed of two or more sets of equally spaced parallel lines intersecting each other. The gml:RectifiedGrid defines another kind of grid. The points of a rectified grid refer to geographic locations. The element describes the position of the origin of the grid along with the position of the offset vectors that determine the spacing of the points of the grid. In the above example the RectifiedGrid starts at the origin (580000, 4500000) and the spacing is 10 units. gml:Grid dimension=2 gml:limits gml:GridEnvelope gml:low0 0/gml:low gml:high5 5/gml:high /gml:GridEnvelope /gml:limits gml:axisNameu/gml:axisName gml:axisNamev/gml:axisName /gml:Grid Fig. 4. An instance of a simple Grid. gml:Rectified Grid dimension=2 gml:limits gml:GridEnvelope gml:low580000 4500000/gml:low gml:high600000 45 20210/gml:high /gml:GridEnvelope /gml:limits gml:axisNamex/gml:axisName gml:axisNamey/gml:axisName gml:origin gml:Point gml:id=orig1 gml:coordinates580000, 4500000/gml:coordinates /gml:Point /gml:origin gml:offset Vector10, 0/gml:offsetVector gml:offsetVector 0, 10/gml:offsetVector /gml:RectifiedGrid Fig. 5. An instance of a simple RectifiedGrid. . The SVG approach As already mentioned, both XML and GML can encode the most mon methods to represent real world, utilizing vector and field models. In Geomatics, visualization is as important as encoding. It does not make sense to encode geometric features without displaying them on a map. This is undertaken by SVG, which is an XMLbased visualization tool. As implied by its name, SVG was designed for the visualization of vector data. The SVG working draft (Jackson, 2021) enriches the content of the previous specifications allowing for the visualization of vector data in various ways. Elements like circle, rect, ellipse, line, polyline, path, textpath and text, which describe geometry can be bined with elements that define gradients, filters, animation, linking, scripting, flowing text, streaming, progressive rendering, vector effects, audio, video, etc. Although SVG specification defines an image element, which can be used to embody a raster image into an SVG file, it is evident that there is nothing mon between XMLbased structures resulting from the SVG specification and raster images. Raster images are neither text based nor human readable. They 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. In order to utilize the information that a raster image holds, a reconstruction is needed as shown in Fig. 2 (Antoniou and Tsoulos, 2021). Images constitute the most valuable sources for geographic data acquisition and the extraction of information from raster datasets is carried out with the use of mercial software. Processes like image preprocessing, enhancement, analysis, classification, vectorization and visualization are performed in mercial environments since there are no corresponding freeware applications. In order to work in an ‘‘open’’ environment for the creation of SVG or GML elements from raster datasets, we should follow the alternative paths/ways shown in Fig. 6. The first step is the transformation of the raster image to XML encoding. This can be achieved with the use of any programming language, which can easily depose threeband raster datasets. However, when it es to multiband datasets, data from all bands must first be saved in an ASCII file and then in XML. Provided that XSLT is ideal in manipulating XML documents, XSLTtemplates can be built for the transformation of XML into classified XML datasets based on pixel values and remote sensing elements. The resulting document can be used either for the generation of GML data stored with the Grid functions or for the creation of SVG image files rendered on a cell by cell basis. The SVG image files can be further processed with XSLT exploiting the SVG specification ponents (. transformations, matrices, etc.), for image processing or for the creation of more sophisticated data like georeferenced or orthorectified SVG image files. The SVG image files can be transformed into GML data or SVG elements. This is implemented through a raster to vector transformation bearing in mind that we do not deal with raster images but with a text file that describes explicitly a raster dataset. In addition, at any point of the process ECMAScript can be used for SVG element digitization and subsequent transformation into GML entities using XSLT. 4. Application A case study was carried out in order to verify the efficiency of the method and evaluate the results. The application elaborates on the ways and methods used to achieve a truthful and accurate oute, which is subsequently pared with the results taken with the use of proprietary software. The dataset used is a subset of a Landsat TM scene showing an island. The raster dataset has seven bands with 25m ground resolution (164 190 pixels). Only six bands were used from the dataset since the sixth band is the thermal band and it is not normally used in a classification process. Each band is stored as a different