The field of Geographical Information Systems (GIS) has experienced a rapid and ongoing growth of available sources for geospatial data. This growth has demanded more data integration in order to explore the benefits of these data further. However, many data providers implies many points of view for the same phenomena: geospatial features. We need sophisticated procedures aiming to find the correspondences between two vector datasets, a process named geospatial data matching . Similarity measures are key-tools for matching methods, so it is interesting to review these concepts together. This article provides a survey of 30 years of research into the measures and methods facing geospatial data matching. Our survey presents related work and develops a common taxonomy that permits us to compare measures and methods. This study points out relevant issues that may help to discover the potential of these approaches in many applications, like data integration, conflation, quality evaluation, and data management.

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An analysis of almost 200 references has been carried out in order to obtain knowledge about the DEM (Digital Elevation Model) accuracy assessment methods applied in the last three decades. With regard to grid DEMs, 14 aspects related to the accuracy assessment processes have been analysed (DEM data source, data model, reference source for the evaluation, extension of the evaluation, applied models, etc.). In the references analysed, except in rare cases where an accuracy assessment standard has been followed, accuracy criteria and methods are usually established according to the premises established by the authors. Visual analyses and 3D analyses are few in number. The great majority of cases assess accuracy by means of point-type control elements, with the use of linear and surface elements very rare. Most cases still consider the normal model for errors (discrepancies), but analysis based on the data itself is making headway. Sample size and clear criteria for segmentation are still open issues. Almost 21% of cases analyse the accuracy in some derived parameter(s) or output, but no standardization exists for this purpose. Thus, there has been an improvement in accuracy assessment methods, but there are still many aspects that require the attention of researchers and professional associations or standardization bodies such as a common vocabulary, standardized assessment methods, methods for meta-quality assessment, and indices with an applied quality perspective, among others.

A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

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