On the Use of Deep Learning for Geodata Enrichments - Archive ouverte HAL
Chapitre D'ouvrage Année : 2021

On the Use of Deep Learning for Geodata Enrichments

Résumé

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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Dates et versions

hal-03594467 , version 1 (02-03-2022)

Identifiants

Citer

Alaeddine Moussa, Sébastien Fournier, Bernard Espinasse. On the Use of Deep Learning for Geodata Enrichments. Interdisciplinary Approaches to Spatial Optimization Issues, IGI Global, pp.182-192, 2021, Advances in Geospatial Technologies, ⟨10.4018/978-1-7998-1954-7.ch010⟩. ⟨hal-03594467⟩
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