Time Machine at the Academy of Arras

Lecture Series “The Sustainable City, From the Past to the Present” by the Académie des Sciences, Lettres et Arts d’Arras (FR)

Date: 26 February 2020
Time: 18.00 o´clock
Language: French
Venue: Hôtel de Guînes

The Académie des Sciences, Lettres et Arts d’Arras was founded in 1737 and erected as a royal academy in 1773. It is a member of the National Conference of Academies of Science, Letters and Arts, under the aegis of the Institut de France.

From 2019 to 2020, the Academy organises a series of lectures dedicated to contemporary societal questions under the title “The Sustainable City, from the Past to the Present.”

Within this series, Valérie Gouet-Brunet (Research Director at the Institut national de l’information géographique et forestière (IGN), member of the Time Machine Executive Board and French Time Machine Ambassador) presents the Time Machine project from the point of view of “Time Machine and the Big Geodata of the Past”.


The massive deployment of digitization technologies and the increasing availability of digital data describing the past have made the big data of the past a major challenge for research in information science and digital humanities. They represent a huge potential for knowledge and understanding of our social, cultural and geographical heritage and its evolution over time. In this context, we will present the large-scale research initiative Time Machine, which aims to develop the science and technology for extracting and processing these massive data from the past through a distributed information system. A large part of this data can be associated with spatial information, providing a common framework for analysis, structuring and exploration to these massive data of the past, bringing to light what can be called the Big Geodata of the Past. We will thus present solutions developed at the French mapping agency (IGN), which for several years has been developing several research work and tools for the acquisition, processing, analysis and restitution of data from the past in relation to their spatialization.