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Geospatial datasets representing our natural environment is widely used and applicable for various engineering, environmental, and social processes. Digital Terrain Models (DTMs) describing the terrain relief are among the most relevant sources for quantitative and qualitative environmental evaluation. Using simultaneously several geospatial datasets requires addressing the issue of data integration/comparison. This chapter introduces three novel algorithms for these tasks, which deal with the problem of achieving terrain continuity and completeness despite discrepancies that might exist among the different data sources. The main novelty of these algorithms is in using localized topographic structure of the terrain described by the DTMs, rather than trying to match whole datasets based solely on their coordinates. Several applications based on the suggested algorithms demonstrate their usefulness and applicability for establishing a reliable tool for environmental control processes. [ABSTRACT FROM AUTHOR]

A substantial amount of data about geographical entities is available on the World-Wide Web, in the form of digital maps. This paper investigates the integration of such data. A three-step integration process is presented. First, geographical objects are retrieved from Maps on the Web. Secondly, pairs of objects that represent the same real-world entity, in different maps, are discovered and the information about them is combined. Finally, selected objects are presented to the user. The proposed process is efficient, accurate (i.e., the discovery of corresponding objects has high recall and precision) and it can be applied to any pair of digital maps, without requiring the existence of specific attributes. For the step of discovering corresponding objects, three new algorithms are presented. These algorithms modify existing methods that use only the locations of geographical objects, so that information additional to locations will be utilized in the process. The three algorithms are compared using experiments on datasets with varying levels of completeness and accuracy. It is shown that when used correctly, additional information can improve the accuracy of location-based methods even when the data is not complete or not entirely accurate. [ABSTRACT FROM AUTHOR]