Abstract
This chapter deals with information simplification after the union of several information layers within a geographic information system (GIS). The challenge is to have better visualization (spatial homogeneity) while keeping as much information as possible (semantic information). These two objectives are opposing. Each layer has a set of concepts attached to an ontology, allowing the computation of a semantic distance used to select the information. Each object (called an instance) in a layer is annotated with the concept of the layer and also with its spatial information (shape, localization, etc.). The proposed approach uses a two-phase multiobjective local search in an ascendant way starting from the most complete set of concepts or in a descendant way starting from the most simplified set of concepts. In this chapter, we use this technique for environmental applications in order to determine ecological units based on environmental and topological layers. These units are used to identify isolated or threatened ecosystems in tropical forests. We compare the quality of the results and the computation time with other approaches, such as genetic algorithms.
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Grandchamp, E., Fonseca-Cruz, E. (2014). A Two-Phase Multiobjective Local Search for GIS Information Fusion: Spatial Homogeneity and Semantic Information Tradeoff. In: Popovich, V., Claramunt, C., Schrenk, M., Korolenko, K. (eds) Information Fusion and Geographic Information Systems (IF AND GIS 2013). Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31833-7_10
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DOI: https://doi.org/10.1007/978-3-642-31833-7_10
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