In the past, spatial information mainly functioned as a tool for record-keeping and visualization. It helped people understand what already existed. For example, it was useful for identifying underground utilities, supporting maintenance, or displaying facility locations.

Today, its role is becoming much more strategic because it is increasingly connected to prediction, simulation, and decision-making. All data includes a spatial element, and when that data is linked within a spatial information context, it becomes much more useful for integrated analysis. With AI, spatial information is moving from simply seeing to being able to help predict. It can be used to forecast traffic flows, analyze real estate values, simulate disaster response, and support public policy.

This becomes even more important in new fields such as autonomous driving, robotics, and urban air mobility, all of which rely on digital twin technology to connect the physical world and the digital world. In the age of AI, spatial information is becoming foundational infrastructure.

Customers no longer want systems that simply show maps or store data. In the past, spatial information systems were mainly heavily focused on visualization and also often required specialized knowledge, making them difficult for non-experts to use.

Now, users want systems that support action. They want analysis, prediction, prevention, and scenario-based decision-making support. They want to know not only where something is located, but also whether risks can be predicted, how a certain event will unfold, and what response will be most effective.

A simple example is navigation. In the past, navigation systems mainly suggested the shortest route. Now, they increasingly use real-time data to determine the fastest route and support more dynamic decision-making. In the same way, digital twin systems are changing users’ expectations in public infrastructure and industrial fields. In other words, the role of spatial information systems is evolving beyond simple visualization into intelligent platforms that help users predict and prevent future problems.

 

One powerful example can be found in digital twin systems used in industrial complexes. In the past, the focus was on visualizing facilities and tracking maintenance status. Now, the demand is very different. Users want to predict the possibility of fire, monitor changes through sensors, simulate how hazardous materials may spread, and assess which surrounding areas may be affected.

Therefore, the system no longer ends with observation. It expands into prediction, response planning, and decision-making support. This is a very important change in the role of spatial information.

AI and new technologies are transforming spatial information from static datasets into real-time systems, and from visualization tools into intelligent decision-making platforms. The industry is moving toward digital twin environments that can reflect changes in the physical world and support immediate responses.

These technologies are also lowering barriers to entry. For example, one of our goals is to enable users to create or interact with maps through natural language. This makes spatial information more accessible even to non-experts.

The greatest impact will appear in areas such as smart cities, autonomous driving, urban air mobility, disaster and safety management, logistics, and delivery.

Natural Language-Based Mapping(chat based Starbucks density analysis)

One of the earliest milestones was the development of the Korea Land Information System, or KLIS, in 2004 and 2005. It was rolled out nationwide in 2006 and is still in operation today. This remains one of our most meaningful achievements, because it plays a role as one axis of national digital-based administration and, furthermore, is regarded as an international reference model in the related field.

As government-related spatial projects increased, we came to focus more on B2G and accumulated a great deal of practical knowledge. Over time, we recognized that various projects required similar components. So we began to bring these capabilities together and modularize them sequentially. This led to the launch of GeOnPaaS in 2019, which marked an important transition from SI and maintenance work to a platform-based model.

Since then, the company has grown rapidly by building platforms based on the modularized business. GeOnDT expanded these capabilities into digital twin and 3D spatial services. GeOnAI emerged as AI became an essential element and was added as an intelligence layer on top of the platform. In 2023, we acquired and merged with the GIS business division of Naver Systems, strengthening our capabilities in spatial data production and aviation. This also supported the development of solutions for GeOnAirX and for securing a leading position in future UAM business, and we plan to continue developing it.

Going forward, we will continue to advance our existing B2G strengths while deepening our AI and UAM capabilities and expanding overseas.

It is more appropriate to understand them as a connected platform ecosystem rather than as individual products. For example, GeOnPaaS is the foundation of our company’s integrated platform. It is our 2D spatial information development platform, and it provides core functions through APIs so that developers can build spatial services more efficiently. GeOnDT is built on that foundation and expands into 3D and digital twins, enabling simulation, prediction, and scenario analysis. GeOnCity focuses on administrative functions for local governments. GeOnAI adds an intelligence layer, making spatial services easier to use through conversational interfaces and AI-based analysis. GeOnAirX expands our capabilities into aviation and UAM, including traffic control, vertiport management, and simulation. We also have cloud-based subscription services such as Mappick and Mappick Gov. These are not separate offerings. They are stages within a broader roadmap, and as technology advances, each solution strengthens the others.

 

GeOnPaaS is a cloud-based spatial information development platform. It modularizes the functions needed for spatial service development and provides them through APIs, documents, wizard tools, and widgets. Its purpose is to reduce the burden on developers so that they do not need to build everything from scratch.

It is not only for developers. General users can also upload files, generate map data, create maps, and convert them into URL-based applications. This is very important because one of our goals is to make spatial information easier for non-experts to use.

From a business perspective, GeOnPaaS is provided both as a subscription service and as a server-unit installation solution. It is also provided through Naver Cloud. In other words, if any non-expert has a business model using spatial information, it is possible to build a system that can easily provide services through the GeOnPaaS platform. As part of building the ecosystem, we have also provided it free of charge to several universities in Korea.

 

The digital twin plays a central role because it enables organizations not only to visualize assets and environments, but also to simulate future scenarios. Through GeOnDT, users can perform view analysis, sunlight analysis, flood simulation, and hazardous material diffusion analysis in a 3D environment.

It can also visualize underground infrastructure and support virtual excavation before actual work begins. In urban planning, it can help estimate development density, evaluate design constraints, and improve both planning and economic feasibility analysis. It is also connected with BIM data, which expands its value in construction and asset management.

This is why the digital twin has become very important. It moves spatial information beyond simple display into actual scenario-based planning and decision-making support.

The practical context is that most of our work is carried out within closed administrative networks where open public AI services cannot be used. Therefore, we developed an AI solution based on an open-source large language model that can operate on-premises within a secure internal environment.

But beyond that, the core concept is accessibility. Spatial information has traditionally been difficult for non-experts to use. Through GeOnAI, users can interact with maps through natural language. They can search for locations, request analysis, create layers, and perform tasks in a conversational manner.

For example, a user may ask to show Starbucks locations in Seoul and then request a density analysis, or ask to identify old buildings in a certain area and display them by age category. This makes spatial tools much more intuitive for planning, asset management, and business analysis.

In that sense, GeOnAI is not just an AI add-on function. It is a step that makes spatial information available to a much broader group of users.

Historically, B2G has been our strongest area. In addition, we are also responding to demand in the private sector and to technological advancement. B2B means enabling other companies to build and commercialize services on top of our platform, and B2C is also a future possibility, especially as AI makes spatial tools more accessible.

GeoAI is particularly attractive in that context because it lowers the barrier to use. At the same time, GeOnPaaS remains a structural foundation for expansion because a very large number of services can be built on top of it.

 

It is very important. GeOnAirX has already moved beyond the demonstration stage and is entering the commercialization stage. We see it as a key driver of the next stage of growth.

It was developed to support UAM-related activities such as vertiport installation and operation, control systems, pilot services, and spatial analysis of routes and locations. Because UAM does not operate at high altitudes like conventional aviation, but rather within urban environments, precise urban data and digital twin capabilities are essential.

This platform can be used not only for future UAM operations, but also for current drones, monitoring systems, and other aviation-related applications. It supports simulation, controller screens, risk alerts, and integrated analysis of conditions such as population density, medical facilities, transportation demand, and environmental impact.

We are already carrying out cooperation with institutions such as Korea Airports Corporation and Korean Air, and we are also cooperating with Daegu City, which has been designated as a UAM-specialized zone. We expect pilot projects to begin in Korea around 2028 and commercialization to take place around 2030.

Our clearest strength is the combination of practical public-sector implementation experience and new technologies. We are not simply talking abstractly about advanced platforms. We have deep experience in building and operating national and local government systems, and that experience provides a real foundation for our new technologies.

This is difficult to replicate. Our GeOn platform product family reflects decades of accumulated know-how, and it is strengthened by the trust we have built with customers, the quality of our personnel, and more than 20 years of continuous growth and financial stability as a listed company.

 

Yes, that is correct. Because public-sector businesses are executed through budgets, it is both a strength and a limitation.

On the positive side, it provides stable revenue, predictable business opportunities, and strong credibility in the spatial information industry. It has also allowed us to become a technology leader in this field.

On the other hand, dependence on government budgets can restrict growth and make it difficult to expand into new areas. The public-sector foundation remains central, but we are building a structure that goes beyond it through platforms, AI, UAM, and overseas expansion.

The Ethiopia LIMS project was particularly meaningful. It was not simply system development. It was the implementation of a national-level platform that reflected institutional, administrative, and regional realities.

That experience confirmed that technology alone is not enough. Success depends on understanding local systems, coordinating stakeholders, and adapting solutions to actual field conditions. In many markets, because telecommunications infrastructure and institutional environments differ from those in Korea, we had to simplify systems, reduce database complexity, and localize our approach.

What we learned is that our capabilities work best especially in countries that are in the early stages of digital transformation, particularly where there is demand for land administration modernization, digital cadastre, address systems, and national spatial information platforms. Our true strength is not merely technology, but the ability to combine technology, institutions, and operations.

 

We see strong potential in Southeast Asia, Central Asia, Africa, and other regions where demand for digital transformation is increasing. These are places where Korean-style land and spatial information systems can still have very high relevance.

At the same time, we are also interested in markets such as Europe, where opportunities may arise through partnerships with domain-specialized companies in fields such as urban development or energy. In such cases, our spatial technology will be combined with local industrial expertise.

In the short term, infrastructure-level digital platform projects will remain a key axis of our overseas growth strategy. At the same time, international interest in new fields such as GeOnAirX is also increasing.

 

The next stage of growth will come from connecting our existing strengths with new opportunities. National and local government systems remain an essential foundation and must continue to advance. On top of that, GeoAI is becoming an increasingly important growth area because it can combine spatial data and AI and expand into new application fields.

We also see strong future potential in new areas that WAVUS has not yet sufficiently explored, such as UAM, autonomous driving, robotics, and the marine, cultural, environmental, and disaster management sectors.

From our perspective, the long-term direction is clear. We must continue to strengthen B2G, deepen the integration of AI and spatial platforms, expand future mobility capabilities, and use this integrated structure as the basis for overseas growth.

 

To be honest, I feel there is still room for the company’s current value to be more fully recognized. Because there were areas where we did not respond sufficiently in terms of publicity, IR, and promotion, part of the responsibility lies with us. At the same time, I think the market has been conservative in evaluating the company.

What I hope many people will understand is that WAVUS is not merely a company with a long track record in the public sector. We are building the next stage of growth through platforms, AI, digital twins, UAM, and overseas expansion. We believe that this broader direction has not yet been sufficiently reflected.

 

For more information please visit: http://www.wavus.co.kr/en/