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  4. A deep learning approach to Cadastral Computing
 
conference paper not in proceedings

A deep learning approach to Cadastral Computing

Ares Oliveira, Sofia  
•
di Lenardo, Isabella  orcid-logo
•
Tourenc, Bastien  
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July 11, 2019
Digital Humanities Conference

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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Type
conference paper not in proceedings
Author(s)
Ares Oliveira, Sofia  
di Lenardo, Isabella  orcid-logo
Tourenc, Bastien  
Kaplan, Frédéric  
Date Issued

2019-07-11

Subjects

cadastral computing

•

deep-learning

•

segmentation

•

transcription

•

gis

•

information extraction

URL
https://dev.clariah.nl/files/dh2019/boa/0691.html
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DHLAB  
Event nameEvent placeEvent date
Digital Humanities Conference

Utrecht, Netherlands

July 8-12, 2019

Available on Infoscience
July 16, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/159139
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