A deep learning approach to Cadastral Computing - HAL-SHS - Sciences de l'Homme et de la Société
Communication Dans Un Congrès Année : 2019

A deep learning approach to Cadastral Computing

Résumé

This article presents a fully automatic pipeline to transform the Napoleonic Cadastres into an information system. The cadastres established during the first years of the 19th century cover a large part of Europe. For many cities they give one of the first geometrical surveys, linking precise parcels with identification numbers. These identification numbers points to registers where the names of the proprietary. As the Napoleonic cadastres include millions of parcels , it therefore offers a detailed snapshot of large part of Europe’s population at the beginning of the 19th century. As many kinds of computation can be done on such a large object, we use the neologism “cadastral computing” to refer to the operations performed on such datasets. This approach is the first fully automatic pipeline to transform the Napoleonic Cadastres into an information system.
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Dates et versions

hal-03988983 , version 1 (14-02-2023)

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  • HAL Id : hal-03988983 , version 1

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Sofia Ares Oliveira, Isabella di Lenardo, Bastien Tourenc, Frédéric Kaplan. A deep learning approach to Cadastral Computing. Digital Humanities Conference, Jul 2019, Utrecht, Netherlands. ⟨hal-03988983⟩
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