Japan uses DX often. Most entail introducing IT and AI solutions and cost-cutting by a few tens of per cents. Any company can add tools, and if AI lowers prices, it will shrink the market and fight for the Red Ocean Market at a greater cost. Their DX approach leverages AI and proprietary algorithms to create unimaginable businesses with existing technologies and techniques. It offers a true paradigm change and value revolution, saving tens or hundreds more time than previous construction methods.
Orbital Net was founded by a nearby surveying and design firm’s IT section. It subcontracted services to the parent company and invested the earnings in research and development to transfer indigenous technologies to the parent. In its third year, the parent firm changed its management philosophy, prompting Orbital Net to slip down the management ladder and rethink its business plan. They did a “management buyout” two years ago, bought the original company’s management rights, and started over as Orbital Net.
The Principal Operational Areas
The business primarily operates in two sectors. The first is FOSS4G (Free Open Source Software for Geo-Spatial), a business support system built on Open Source Geospatial Information Software. Solutions can take many shapes, from dedicated systems that integrate multiple pieces of software to customizing particular software based on customer requirements. They primarily focused on local government operations in the past, but more lately, they have created more all-purpose goods and services. These are meant to be purchased online.
Compared to their commercial counterparts, these solutions and services provide more flexible and customer-budget-oriented offerings. These solutions consider the rising need for geospatial Open Source Software in recent years. The cost of lower licenses for the first deployment and scaling up benefits the customer, while the benefit to us is the cost of lower software purchases.
AI-based solutions are the second. Using wide-area aerial and satellite photos as the primary source, AI techniques like object identification and segmentation are used to identify target geographic objects and store their location as geospatial information in the form of points and polygons.
The primary targets so far have been map components, including streets and buildings, parking lots, solar panels on buildings, and automobiles. The system’s unmatched processing speed—the AI completes tasks that used to take a person a day to complete—is one of its key features. Therefore, it focuses on large-scale decoding jobs previously thought to be humanly impossible.
They have created and are the owners of inference engines like Ortho Enlarger for super-resolution aerial and satellite pictures, Geo Detector for geographic feature shape tracing, and Geo Detector for geographic feature identification. These are currently employed as internal production tools for decoding aerial and satellite images.
Similarity To Current Trends
Open source, open data, DX (Digital Transformation), and 3D are current trends in geospatial information. Since its founding, Orbital Net has focused mainly on open-source and open-source data. Therefore they are right on trend. Additionally, in the past, expert technicians would have visually decoded aerial and satellite pictures into data while developing original map content. However, in recent years, efforts to use AI to decode them have increased automatically. It may be claimed that Orbital Net has been ahead of its time because they have been working on this technology since they were founded. They do not actively work on 3D but produce unique content and show 3D-related open sources and open data.
As there are many manufacturing and thinking components in this profession, which includes surveying, construction consulting, and geographic information, technical advancement is slower than in other fields. Rapid technical innovation is unlikely to happen, in particular, because they cannot actively use modern technology for work related to public works. After all, requirements are fixed, and many of their clients are local SMEs. Orbital Net is unique among businesses because it is free to experiment with cutting-edge technology without being constrained by common sense or accepted norms.
The Distinguishing Quality Character
Orbital Net concentrates on providing services that rival businesses do not. They have few rivals primarily because of this. Sometimes customers turn to them because other businesses cannot provide a solution or because it would be technically challenging for them to do so. Could Orbital Net not handle it? It’s also true that they get a lot of questions saying.
The name of their AI solution is “Geo Detector.” This program employs artificial intelligence to interpret maps and their components from aerial and satellite images. It is positioned as a content generator that produces high-speed, high-volume, and highly accurate information all at once and is a detection tool that precisely identifies target geographic features from aerial and satellite photographs.
End-users have hitherto had to rely on field surveys or the visual interpretation of aerial pictures to learn more about rooftop PV systems. AI-based aerial photo interpretation recognized and located buildings with solar panels over a vast area of 6000 km2 in just six seconds using their Geo Detector. They also learned they might perform direct mail sales and locate addresses within a month.
Previously, this would not have been possible, but thanks to Orbital Net’s solution, the businesses may now accomplish their goals.
AI-based super-resolution aerial and satellite imagery solutions are part of Orbital Net’s future objectives. Exceptional clarity is provided via super-resolution processing powered by AI from low-resolution imagery. High-resolution photographs are expensive and complex to come by. Other benefits of super-resolution include the capacity to see more fine details in things by sharpening images. They also intend to provide SaaS-based AI-based object detection and shape racing technology based on aerial and satellite images, which they have been using as a production tool internally.
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