Towards a local, dated and thematic digital twins factory

The notion of digital twin can be ambiguous because it can be defined in various ways. These last months have seen the emergence of many global digital twin initiatives. The challenge of these global digital twins is to create a qualified digital replica model of our planet, making it possible to monitor, simulate and anticipate natural phenomena and human activities. The target users are either scientists or decision makers. Through the digital twin, they have access to a digital representation of an environment using all available spatial and non-spatial data accompanied with a set of physical and statistical models to calculate projections, replay past events or simulate future ones.   Refining and evaluating the accuracy of these projections is a major challenge for digital twins. In addition to the knowledge of physical modeling, suitable data must also be available. Complementary to the global approach, the notion of local and dated digital twins appears then to be essential. Considering a digital representation of a restricted geographical area of interest (an urban area, watershed, coastline, etc.) allows to access to very high-resolution "fresh" data in 2D and 3D, in-situ data and small-mesh physical model. This user-centered and naturally thematic approach responds more finely and more pragmatically to the objectives presented. These local, dated and thematic digital twins are by essence ephemeral: a way to meet a specific need.   The challenge is therefore to setup a Digital Twin Factory (DTF). This DTF relies on a data lake, a high computing capacity via clouds and/or HPC and has thematic algorithms and methodologies able to generate registered and coherent layers of information in order to enrich a datacube from which physical indicators can be computed spatially. Thanks to its thematic, local and on-demand characteristics, the DTF can mitigate the need to have an universal model of metadata. This datacube allows to apply local physical and artificial intelligence models. The overarching architecture of the DTF will be presented. Specific examples on coastal, urban and risk topics will also be presented. These digital twins rely on a large number of expertises in both data and modeling involving various French (CNES, IGN, SHOM, IRD, CEA, INRAE, METEOFRANCE, CERFACS, BRGM, etc.) or international organizations (ESA, NASA, NOAA,…). For coastal areas, the goal is to well describe the bathymetry topography continuum by taking into account the intertidal zones and the specialized dynamic models together with 3D coastal land cover characterisation. For urban areas, the ambition is first to automatically produce a qualified 3D map together with its additional layers of information: 3D objects and related semantics (land cover and land use) including temporal dynamic, thermal information. Then, for issues related to the management of natural risks (such as floods or fires) similar data layers can be used. Finally, new hypothesis can be injected in these digital replica and multiple scenarios can be applied to assess causal relationship between hypothesis and prediction. Very promising results will also be presented.