A Symbiotic Relationship Supporting Sustainability

While geospatial infrastructure is required to model and simulate changes in the real world and feed them into the Digital Twin ecosystem, a digital replica at the national level can open doors to a whole range of geospatial applications in multiple sectors. Together, these two can support sustainable economic practices.

Digital Twin

The Fourth Industrial Revolution (4IR) is leading to an explosion in the volume of geospatially referenced data linked to different value-chains. This will help in providing a new and greater understanding of how processes work and how our society behaves. However, data is not the endpoint. The true value of data lies in its use to derive knowledge to meet current and future needs. Most of the world’s greatest challenges are time and place-related, which makes geospatial and location data extremely important. Thus, geospatial knowledge is crucial for solving these challenges and needs to be placed at the heart of tomorrow’s sustainable digital society.

“Geospatial knowledge relies on rapidly evolving ecosystems of data that combine different sources,” Alison Rose, Chief of Place, Space, and Communities Division, Geoscience Australia, told Geospatial World. The Big Data analytics, Artificial Intelligence (AI), and modeling communities are vital to the creation of spatial knowledge, and are no longer just customers — they have the same standing as data providers in the geospatial space. The evolving digital ecosystem has created the need for a next-generation geospatial infrastructure that embraces automation, dynamicity, and real-time delivery of knowledge.

Why geospatial infrastructure?

Geospatial infrastructure is a “system-of-systems” that interconnects people, processes, data, and technology. It has emerged as a vision to frame these increasingly integrated components of the geospatial industry. These components have advanced dramatically since maps and images of the Earth were first digitized and visualized on computers. Just as one cannot create geospatial data without hardware or visualize and analyze it without software, one cannot develop solutions that deliver a greater geographic awareness without each of these components of geospatial infrastructure.

Today, the Internet and cloud computing are transforming the way organizations manage, analyze, and share data and collaborate through an interconnected network of systems and portals. Cloud and edge computing, AI and Machine Learning (ML), paired with Internet of Things (IoT) devices are changing how data is collected, processed, and managed. Further, open platforms are enabling multiple levels of interoperability, and secure and highly scalable web services are fueling the dynamic maps, apps, and information products that help scientists, decision-makers, and the general public understand the world around them. Geospatial infrastructure can connect multiple organizations across borders, sectors, and jurisdictions, thereby reshaping the way we collaborate for better outcomes.

Knowledge Management Cognitive Pyramid
This knowledge management cognitive pyramid demonstrates the relationship between data and knowledge. 4IR technologies enable knowledge to be generated automatically, thereby improving decision-making. Source: Adapted from DIKW Model for Knowledge Management and Data Value Extraction

Geospatial infrastructure and digital twins

Since the essence of the Fourth Industrial Age lies in embracing automation, data interoperability, data exchange, and manufacturing technologies, the Digital Twin technology lies at its core. A Digital Twin is a virtual replica of a physical asset, process or service. It acts as a means to link digital models and simulations with real-world data to create new possibilities for better creativity, competitive advantage and human-centered design. The Digital Twin concept cuts across various sectors, changing the traditional approaches that required designing to be exclusively done in the industrial world, and bringing in a more virtual system-based design process. The implementation of these digital replicas helps organizations derive better insights, improve product performance, and enhance operational and strategic decisions.

However, creating and maintaining an efficient Digital Twin requires overcoming challenges concerning data exchange, governance, and interoperability. “While it is difficult to build a Digital Twin, the biggest challenge is to maintain it. The key questions concerning a Digital Twin are: how will you make it, how will you keep it up to date, how will you maintain it, and whether you have a good maintenance process that is not just limited to administrative maintenance but covers reallife maintenance,” says Ingrid Vanden Berghe, Director, National Geographic Institute, Belgium. To overcome these challenges, geospatial infrastructure serves as the foundation of Digital Twin, leading to greater demand for authoritative, accurate, updated and accessible data platforms.

Geospatial infrastructure is primarily the responsibility of national geospatial organizations (national mapping agencies, survey organizations, geological agencies, Earth Observation agencies, etc.), which have a critical role to play in reinforcing the Digital Twin technology in varied economic sectors. Geospatial data, when facilitated in real time by national geospatial organizations, results in a continuously updated Digital Twin — a system-of-systems that is accessible anytime from anywhere by all stakeholders. For instance, to build the Digital Twin of a city, a large-scale base map, topography and terrain data, a map of underground networks of cables and pipes, a map of transport networks and buildings, precise positioning data from GNSS systems and IoT sensors, spatial data maintained by municipalities and city departments, along with non-spatial content such as zoning regulations and laws, emergency services contact information, and more, needs to be available and usable. Additionally, a twin is often built by using latest modeling technologies and applications, such as a standard-based semantic city information model and a reality mesh model. So, to build a viable digital replica, geospatial infrastructure provides reliable, continuously updated and maintained centralized datasets, as per the requirements of the models. It also enables easy data interoperability and helps in maintaining data standards.

To build digital twins of different sectors, cities, or even countries, a strong geospatial infrastructure is of great importance. Hence, investments have to be made in building and strengthening this infrastructure, including in the organizations that are responsible for managing, processing, and automating the core geospatial data for the surface infrastructure assets, both above and below. Without authenticated data from geospatial infrastructure, building a Digital Twin of a city for public safety and citizen services, for health and governance, or for transport infrastructure networks, is an incomplete process. “The adoption and scaling of a Digital Twin at the national level requires strengthening of the national geospatial infrastructure and turning it into a Digital Twin infrastructure through a demand-driven approach, in which all stakeholders, including the government, academia, knowledge institutes, geospatial industry, non-governmental organizations, and citizens work together to co-create an ecosystem of digital twins at different levels,” says Michel Grothe, Senior Scientist, Geonovum.

Knowledge Management Cognitive Pyramid
This knowledge management cognitive pyramid demonstrates the relationship between data and knowledge. 4IR technologies enable knowledge to be generated automatically, thereby improving decision-making. Source: Adapted from DIKW Model for Knowledge Management and Data Value Extraction

Without authenticated data from geospatial infrastructure, building a Digital Twin of a city for public safety and citizen services, for health and governance, or for transport infrastructure networks, is an incomplete process

How can digital twins benefit the geospatial industry?

Digital twinning is used across various sectors, ranging from manufacturing, healthcare, automobile, retail, public safety, digital cities, architecture, disaster management, defense and intelligence, to engineering and construction. Using varied sensors and cognitive tools and technologies, Digital Twin solutions help improve product design and services. As these digital models are deployed in every industry, many countries around the world are now developing or implementing their own National Digital Twin programs — an ecosystem of connected twins — to democratize data access and enable collaboration between industries and disciplines. The UK and the Netherlands are excellent examples of how countries are using this transformative technology for better outcomes.

Infographic

The relationship between a National Digital Twin and geospatial infrastructure is symbiotic in nature. While geospatial infrastructure is required to model and simulate changes in the real world and feed them into the Digital Twin ecosystem, a digital replica at the national level can open doors to a whole range of geospatial applications in various economic sectors. Within the geospatial industry, there is a lot of data generated in-house, which means there are lots of opportunities for technology professionals as well as entrepreneurs seeking to make their own imprints by enriching that data and integrating it with the national infrastructure for the development of digital twins. This data delivery and integration can lead to many new partnership models, which can directly benefit geospatial players.

How can geospatial infrastructure and digital twins support sustainable economic practices?

Geospatial technology, complemented by advances in digital technologies, plays a big role in enabling circular economy practices. A circular economy is an economic model that is designed to tackle global challenges like climate change, biodiversity loss, etc. Unlike most linear economy businesses that take a natural resource and turn it into a product that ultimately becomes waste because of the way it was designed, a circular economy employs reuse, sharing, repair, refurbishment, and recycling to create a closed-loop system, minimizing the burden on natural resources. The digitization of the built environment due to enhanced computing power, cheaper sensors, IoT devices, advanced analytics, and greater 3D visualization, has the potential to actively contribute towards achieving the United Nations Sustainable Development Goals (SDGs). A circular economy requires products to be connected digitally to the manufacturer, so that the entire product lifecycle can be monitored.

Within the geospatial industry, there is a lot of data generated in-house, which means there are lots of opportunities for technology professionals seeking to make their own imprints by enriching that data and integrating it with the national infrastructure for the development of digital twins

The relationship between a National Digital Twin and geospatial infrastructure is symbiotic in nature. While geospatial infrastructure is required to model and simulate changes in the real world and feed them into the Digital Twin ecosystem, a digital replica at the national level can open doors to a whole range of geospatial applications in various economic sectors. Within the geospatial industry, there is a lot of data generated in-house, which means there are lots of opportunities for technology professionals as well as entrepreneurs seeking to make their own imprints by enriching that data and integrating it with the national infrastructure for the development of digital twins. This data delivery and integration can lead to many new partnership models, which can directly benefit geospatial players.

Benefits of National Digital Twin
Benefits of a National Digital Twin

How can geospatial infrastructure and digital twins support sustainable economic practices?

Geospatial technology, complemented by advances in digital technologies, plays a big role in enabling circular economy practices. A circular economy is an economic model that is designed to tackle global challenges like climate change, biodiversity loss, etc. Unlike most linear economy businesses that take a natural resource and turn it into a product that ultimately becomes waste because of the way it was designed, a circular economy employs reuse, sharing, repair, refurbishment, and recycling to create a closed-loop system, minimizing the burden on natural resources. The digitization of the built environment due to enhanced computing power, cheaper sensors, IoT devices, advanced analytics, and greater 3D visualization, has the potential to actively contribute towards achieving the United Nations Sustainable Development Goals (SDGs). A circular economy requires products to be connected digitally to the manufacturer, so that the entire product lifecycle can be monitored.

In order to minimize environmental impacts in ways that will generate customer loyalty, businesses can use GIS and geo-targeting to map their customers in certain areas and initiate either a buyback campaign or a recycling drop-off campaign at a certain phase of the product ownership cycle

Although the circular economy revolves around SDG 12: Responsible Consumption and Production, all 17 goals are interconnected and can benefit by the circular transition. A Digital Twin, which is a digital representation of a physical asset designed to monitor, control, and optimize its functionality, can enable effective monitoring of products under a circular economy. Through data and feedback, both simulated and real, a digital replica can develop capacities for autonomy and help to learn from and reason about its environment. Geospatial infrastructure, which consists of content/data, maps, apps, and GIS systems apart from the usual IT infrastructure (systems, storage, and networking), serves as the foundation for the Digital Twin.

A circular economy requires products to be connected digitally to the manufacturer, so that the entire product lifecycle can be monitored. For example, a challenge for construction waste remanufacturing is the localization and corporate information communication of the industry chain. The use of digital twins can help in overcoming this issue by building connections among individuals in the industry chain. A twin, which is almost identical to the entity of the regional recycling industry, can be built, linking the physical and the digital remanufacturing industry of construction waste, and identifying dynamic changes in the industry through the analysis of the unified data. Also, these models can assist in tracking, recycling and management of waste materials.

In order to use recycled materials repeatedly, it is important to have insights into the end-to-end supply chain. Geospatial infrastructure offers interactive visualization of big datasets from raw material to distribution. In order to minimize environmental impacts in ways that will generate customer loyalty, businesses can use GIS and geo-targeting to map their customers in certain areas and initiate either a buyback campaign or a recycling drop-off campaign at a certain phase of the product ownership cycle.

The rebought/dropped-off products can be fed back into the production cycle as raw material for building new products. “Circularity necessitates coordination between different stakeholders to make sure that a product/by-product reaches someone who needs it. A Digital Twin can help with coordination by bringing people together, and making sure that they view and use the same data, share the same knowledge, and thereby come to an agreement,” explains Florian Arthur Witsenburg, CEO, Tygron.