There is a lot of discussion in the literature about digital twins. Technology advancements make the development of digital twins much more possible. With robotics, big data analytics, machine learning, etc., it is possible from both a technology and a cost standpoint to develop a twin of a system. Building a virtual model of a physical asset allows scenario planning and testing against data collected by the ever-increasing sensors available today. With IoT, IoB and edge computing and other devices being devised and operationalized for all types of data collection, digital twins are within reach of most industries.
My interest lies with geospatial digital twins. This is the addition of geospatial information and connections to the models. Understand where an asset is critical but it is also important to know when the data was collected and what conditions existed at that time and place. To fully build a digital twin that is geospatially aware, it has to be refined through analyzing the volume of data being collected and that data has to be integrated via geospatial connectedness. Building and leveraging such digital twins will be transformational.
Almost every industry has data or assets that are influenced by location. For some industries, location is more critical. Below are some of those industries as examples and provide an idea of the potential of geospatial digital twins.
Climate change and population growth mean we must respond quickly to disasters. Digital twins will help us build smarter dams, utility networks, emergency response plans, and zoning.
Fires, flooding, and drought are becoming the norm. How can governments and affected organizations respond? Without a deep understanding of all the variables, it is nearly impossible to devise the best response. Understanding place/location and how different elements affect each other is critical. We are collecting the data, now we must use it.
Risk management includes understanding the risk at a location. How can I build a digital twin of an insured power plant so I can prepare and respond to a terrorist attack? What protective features do I require from a museum before allowing insured art to be displayed? From understand where a valuable asset is in relation to other factors, insurance companies are leading the way in transforming how we approach risk. Geospatial digital twins will bring geospatial knowledge with real-time monitoring data for even greater results.
For many years, utilities automated their utility networks from paper maps. The building, managing and storing assets in a geospatial format makes viewing and mapping utility networks easier. However, with new technology, the true digital twin can be created to model all the vast amount of data influencing a network. Weather, slope, vegetation, construction, and more can now be accurately included in and used for scenario planning.
Finding, drilling, moving and refining oil into gas is a complex process. It involves assets located in many far-reaching places but is all connected to the supply chain. A digital twin allows more accurate and faster processing and helps prevent problems before they occur. Image knowing precisely when and where to maintain a pipeline before it malfunctions or understanding the vulnerable hotspots along its path.
Defense and Intelligence
GEOINT is focused on the tie between geography and intelligence. This connection is obvious but is still in many ways in its infancy. The rapid proliferation of sensing devices, the connectedness of the internet and changing geopolitical forces will require geospatial digital twins. How can I build an accurate model of a battlefield without a geospatial component?
Environmental factors have an enormous influence on health. Modeling the factors that contribute to health results will allow for better decisions. Can changing a building design decrease depression? Will changing transportation corridors decrease obesity because of easier access to healthy foods? Today we look at historical data and make educated guesses. Having a smart, dynamic digital twin of systems takes the guesswork and politics out of the decisions.
What is Next?
The good news is that core geospatial infrastructure data is being automated and managed by large organizations in GIS implementations. This often hidden source of base data is a gem for IT departments looking for ways to build smarter models or digital twins of their systems. Locked away in GIS departments and in proprietary structured databases, this geospatial data need to be transformed into useful information. Integrating geospatial GIS data with big data from sensors and other devices will allow a powerful understanding of the system. From that understanding, intelligent action can be taken. When that happens we will begin to see Smart Cities, Smart Conservation, Smart Energy and unimagined other Smart (geospatial) systems.