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In recent years, spatial data infrastructures (SDIs) have gained great popularity as a solution to facilitate interoperable access to geospatial data offered by different agencies. In order to enhance the data retrieval process, current infrastructures usually offer a catalog service. Nevertheless, such catalog services still have important limitations that make it difficult for users to find the geospatial data that they are interested in. Some current catalog drawbacks include the use of a single record to describe all the feature types offered by a service, the lack of formal means to describe the semantics of the underlying data, and the lack of an effective ranking metric to organize the results retrieved from a query. Aiming to overcome these limitations, this article proposes SESDI (Semantically-Enabled Spatial Data Infrastructures), which is framework that reuses techniques of classic information retrieval to improve geographic data retrieval in a SDI. Moreover, the framework proposes several ranking metrics to solve spatial, semantic, temporal and multidimensional queries. [ABSTRACT FROM AUTHOR]

The advent of volunteered geographical information (VGI) has contributed to the growth of the amount of user-contributed spatial data around the world. Spatial data acquired from crowdsourcing environments may contain valuable information which can be useful in other research fields, such as Digital gazetteers, commonly used in Geographic Information Retrieval. Digital gazetteers have a powerful role in the geoparsing process. They need to be kept up-to-date and as comprehensive as possible to enable geoparsers to perform lookup and then resolve toponym recognition precisely over digital documents. The detection of toponyms in digital texts such as social media posts is a bottom line for discovering useful spatially related information such as complaints regarding urban areas. In this context, this article proposes a method for gazetteer enrichment leveraging VGI data sources. Indeed VGI environments are not originally developed to work as gazetteers, however, they often contain more detailed and up-to-date information than gazetteers. Our method is applied within a geoparser environment by adapting its heuristics set besides enriching the corresponding gazetteer. A case study was performed by geoparsing Twitter posts focusing solely on the messages aiming at evaluating the performance of the enriched system. The obtained results were encouraging and have provided a good basis for discussion. [ABSTRACT FROM AUTHOR]