Abstract
Reference spatial data sets represent the least changing natural and anthropogenic features of terrine. As a rule, such data are stored in different scales and most frequently updated consequently starting with a spatial data set of a larger scale (usually base scale) thus later performing an update of data in smaller scales. The generalization of features in a larger scale is one of the major processes employed in the creation and update of spatial data of a smaller scale. In order to effectively carry out works, it is recommended to use automatic procedures and generalization only in those cases when changes in features are significant, i.e. affect the update of features in a smaller scale. The article discusses the relation between changes in polygon features (identify land cover territories in a base spatial data set) and different generalization processes as well as the evaluation of significance of likely changes.
Highlights
National mapping agencies (NMAs) often maintain reference and thematic spatial data sets to represent various spatial data identifying natural and anthropogenic phenomena of the world (Kazemi 2003)
The period from 2009 to 2010 faced the development of Lithuanian digital raster orthophotographic map ORT10LT at a scale of 1:10 000, which served as a base for updating the features of Lithuanian reference spatial data set at a scale 1:10 000 in 2011
The period from 2011 to 2012 has been witnessing the update of Lithuanian reference spatial data set at a scale of 1:50 000 using the already updated spatial data at a scale of 1:10 000
Summary
National mapping agencies (NMAs) often maintain reference and thematic spatial data sets to represent various spatial data identifying natural and anthropogenic phenomena of the world (Kazemi 2003). Each update produces absolutely new features (new data set) having no relation with the earlier feature version Such update process requires high technological and human resources, as it takes time to generalize all features anew, revise the result later and evaluate whether it meets the set requirements. Such generalization is more appropriate for creating rather than for updating a spatial data set based on larger scale data. Feature IDs must be unique throughout the data set and remain unchanged all through the life cycle of the feature (INSPIRE... 2009)
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