GOAL: create the digital twin of a drinking water network to facilitate maintenance and improve hydraulic modeling based on its knowledge.

HOW: by integrating all available data from the network into a digital territory twin, to promote updating and cross-use.


Nature Where ? How ?
Orthophotos Open data DataBase Directory
Orthophotos Specific productions DataBase Directory
Cadastre Open data API
Network data Manager’s GIS DataBase Directory
Network detection Specific productions DataBase Directory
Lidar survey Specific productions DataBase Directory
Plan Topographic plan Specific productions DataBase Directory
Survey of works Specific productions DataBase Directory
Occasional georeferencing Specific productions DataBase Directory


Immersion in the digital twin

Have the Lidar survey of the streets

• obtain a cloud of 3D colorized points with centimetric precision
and georeferenced. It is the digital twin of the territory concerned.
• it is carried out in mobile mapping
• supplemented by pedestrian acquisitions (Backpack, videos)
• possible rural areas can be acquired by drone

Example of a buried fire hydrant

Lidar readings of network parts (keyholes, fire hydrants, etc.)

The network pieces are picked up when building the digital twin.
If necessary, certain sectors can be processed by videos transformed into georeferenced point clouds using GeoCassini.

Import existing networks into the digital twin

Import available data of the network

• The network managers have basic data for their networks either on autocad-type software or in GIS.
• This data is imported into the digital twin with the desired metadata.
• The main supported formats are dxf, dwg, shp, kml, geojson, etc…
• The management of network altitudes is managed at the time of import, either by interpreting existing altitude or depth data, or by defining a reference altitude.

Example of discrepancy that can be found by importing data from the GIS before correction

 Correct network position relative to digital twin data

• The territory’s digital twin represents the centimetric geolocated position of all outcrops in the network.
• It is therefore easy to correct the position of the imported data
to “stick” to reality.
• If the 3D description of the manholes is available, the network
can also be repositioned in 3D, on the real values.

Model the network in 3D

• GeoCassini makes it possible to model the network in 3D, from imported and corrected data.
• 3D modeling can be exported in IFC format; there is no longer any need for specific software for this task.

Example of 3D modeling with the correct diameter which is associated in metadata

Example of a sewerage network, illustrates the visualization of several networks in 3D

Representation of water pipes by laying date

GIS queries and sampling

 It is possible to request the metadata of the network and thus
display the network by nature of the pipes, age of the pipes, diameter of the pipes, depth of the pipes, etc….
• These queries can be synchronized with 3D modeling and IFC export.

Visualization of networks in 3D after vectorization

Manage the detection of networks in the digital twin

• Network detections are based either on specific requests within the framework of the implementation of work, or on large-scale detections within the framework of the legislation on DT/DICT.

• In both cases, the sponsor systematically pays the cost of “field” interventions for the georeferencing of detections. These costs can be eliminated.

• In the context of specific interventions, the use of videos makes it possible to obtain the cloud of georeferenced centimetric 3D points of the detection with information on the nature of the network, trajectory and depth, without further field intervention (the GPS readings are no longer useful). It is then easy to plot the network in GeoCassini and export it in the desired format.

• In the context of large-scale detection: these detections are systematicallycarried out with ground-penetrating radar coupled with gps and “mobile mapping” sensors. The use of digital twins of territories avoids paying for the production of single-use, non-replicable lidar data. This helps to significantly reduce the cost of geodetection and improve its accuracy (the digital twin).

Example of video transformation after detection of point cloud networks

Note: in addition to cost reductions, the impact of this new
methodology on the activity’s carbon footprint is significant
and recognized through the “Solar Impulse Efficient Solution”
label awarded to GeoCassini in March 2021

Manage the verification of work in the digital twin
(blind connections) (EDM)

• The association of a digital twin of territory with the cloud-cloud assembly makes it possible to carry out work checks during the installation of the networks.
• Using a gopro, or a smartphone, the network placed in the trench is filmed, just before filling the trench. (RezoCassini application on googlestore).
• GeoCassini automatically transforms the video into a point cloud. It is then georeferenced with the digital twin’s point cloud, and the record is automatically updated.

Note 1: with this method all the buried and non-visible parts of the network are geolocated with centimeter precision.
Note 2: by combining GeoCassini’s EDM mode with this method, the referencing of the parts placed is automatically taken into account.

Improve DT/DICT procedures from the digital twin

The implementation of these processes makes it possible to considerably reduce the costs of the procedures because:
• The network is known with a better geometry
• The integration of network detections and verifications is fast and precise
• The data available under GeoCassini can be accessed from the GeoCassini marketplace, free of charge or with pricing depending on the network manager’s choice.

Improve the geolocation of disorders

GeoCassini is connected to the augmented reality software vGis which allows:
• to facilitate the marking on the ground of existing networks before intervention
• to visualize the position of the buried networks present in a given sector.
• precisely identify the position of a problem in a pipe.

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