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On the Use of Deep Learning for Geodata Enrichments
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Author(s): Alaeddine Moussa (Aix-Marseille University, France), Sébastien Fournier (Aix Marseille University, France)and Bernard Espinasse (Aix Marseille University, France)
Copyright: 2021
Pages: 11
Source title:
Interdisciplinary Approaches to Spatial Optimization Issues
Source Author(s)/Editor(s): Sami Faiz (University of Tunis El Manar, Tunis, Tunisia)and Soumaya Elhosni (University of Tunis El Manar, Tunis, Tunisia)
DOI: 10.4018/978-1-7998-1954-7.ch010
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Abstract
Data is the central element of a geographic information system (GIS) and its cost is often high because of the substantial investment that allows its production. However, these data are often restricted to a service or a category of users. This has highlighted the need to propose and optimize the means of enriching spatial information relevant to a larger number of users. In this chapter, a data enrichment approach that integrates recent advances in machine learning; more precisely, the use of deep learning to optimize the enrichment of GDBs is proposed, specifically, during the topic identification phase. The evaluation of the approach was completed showing its performance.
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