Spatial Data Planning and Investment – What does this mean and what value does this create for brownfields assets?

Spatial digital twins are gaining exponential momentum every day. Do we understand how existing brownfields assets can manage their spatial data in an effective systematic process that doesn’t involve a large CAPEX investment for large scale field data acquisition and modelling activities? The biggest barrier to adoption of spatial data within the operations is not always the cost associated with capture and storage or access. But a clear understanding of what business value the spatial representation provides the operations.

Typically, this is calculated on an adhoc basis with an aggregated baseline of legacy 3D models from the project handover at a LOD350+ for fabrication purposes and LOD300 for design and coordination purposes. But what we are seeing most of the spatial digital asset comprised of is legacy point cloud data, both structured (TLS) and unstructured (MLS). This accumulation of data in various areas, was either created as part of the design and construction stage or more commonly during a small scale OPEX project that was performed in isolation based on a line item within a larger scope of work. So what does this mean? And how could we ever make sense of this scrambled spatial data mess?

With the democratization of geospatial data capture through various MLS means (Hovermap, NavVis VLX, BLK3602GO) we are staring to see an appreciation of the point cloud again. However, MLS activities will only capture at a certain level of detail. Typically, the Hovermap ST or VLX may get to an accuracy of 10mm with a control network. But for many fabrication and construction/installation activities further accuracy may be required. Plus, it might not make sense to capture the whole project in one data acquisition activity, due to access or budget constraints. How do we understand what level of detail our spatial data is at in a specific area? This might not make sense to do at a tag or asset ID level either. When we talk about point cloud data, we are referring to an implicit dataset that is understood but not defined.

How can we manage spatial data at a tag level? We may have point cloud data that only captures half the asset or the ground or incidental areas that could be valuable but at a lower LOD. The concept of managing space and reporting based on Spatial LOD is new to the industry. But for brownfields assets with a mesh of legacy and new spatial datasets, both geometry and point cloud data, this is the only opportunity to create the spatial baseline and in turn the spatial digital thread for the emerging digital twin.

Spatial Digital Twins need to be adaptable and flexible based on need and the value proposition at that specific point in time that justified the immediate investment. Not in totality, nor as an enterprise implementation. The concept of the digital twin being a system of systems, also needs to apply at a spatial level. ?The system of systems approach at a spatial level, implies that there are several spatial systems for example design and fabrication geometry, reference geometry, temporal geometry and geospatial data including point cloud data and imagery. However, each “system” will be at a different level of maturity based on the perceived value and investment. This needs to be considered and in turn reportable. There also could be an opportunity to create new use cases by the combination of the two spatial systems. For example, equipment may only be modelled at a block level, however the segmented and classified point cloud data of the same equipment shows the part level detail required to enable a decision.

This concept also relates to the spatial digital thread and creates a relationship between the level of detail and connectivity of the digital twin to the business processes that the asset owner is looking to contextualize. This allows the asset owner to ask the question; does this need to be high detail geometry or will segmented, and classified point cloud data suffice? ?Plus by managing the space and not the tag or asset ID, we open up further semantic model opportunities that could never have been achieved using a list of tags and a centroid geocoordinate.?