Hosei University, SSIL, and Pacific Consultants to develop physical AI training materials to address aging infrastructure and shortage of inspectors; selected for NEDO's "GENIAC" program; bridge and sewer inspection data to be used as a common platform for AI and robot learning and evaluation.
Hosei University (Chiyoda-ku, Tokyo; President: Diana Khor; Principal Investigator: Professor Ryuichi Imai, Faculty of Design Engineering), Space Service Innovation Lab Business Cooperative (Chuo-ku, Tokyo; Representative of Board of Directors: Naohiko Jintake, hereafter SSIL), and Pacific Consultants Co., Ltd. (Chiyoda-ku, Tokyo; Representative Director, President Executive Officer: Koko Okano) have been selected as operators for the "Research and Development on Data Ecosystem Construction" under the national project "GENIAC (Generative AI Accelerator Challenge)," led by Ministry of Economy, Trade and Industry (METI) and NEDO (New Energy and Industrial Technology Development Organization), aimed at strengthening domestic generative AI development capabilities and social implementation.
This project develops AI-ready data that can be used for training and evaluating physical AI for infrastructure inspections, including inspection records of bridges and water supply and sewage systems, as well as 3D data. The core outcome is a system for training and continuously providing data for training and evaluating physical AI (AI operating in real spaces through devices such as robots). We support the development and evaluation of inspection AI, autonomous robots, and drones from data.
[Background of this project] Addressing aging infrastructure and labor shortages through data utilization.
Currently, there are approximately 730,000 road bridges nationwide, of which about 240,000 are over 50 years old. If we limit ourselves to bridges for which the construction year is known, the proportion is approximately 44% (as of the end of March 2026). Sewerage pipelines amount to approximately 500,000 km (as of the end of fiscal year 2024), and 25% of municipalities lack civil Engineer or Engineer in their general administrative Dept. (as of April 1, 2025).
Furthermore, records accumulated on-site lack consistency in format, terminology, and management, making them difficult to reference for each facility or use for AI training. Hosei University and the other two organizations will work to support human knowledge and experience with data and AI, creating a foundation for sustainably protecting essential infrastructure.
Source: Ministry of Land, Infrastructure, Transport and Tourism, "Annual Report on Road Maintenance (FY2025)," etc. Details are provided in the attached document. Road bridges are defined as those with a length of 2m or more, and sewer pipelines are defined as those excluding urban sewer channels. The 50-year age since construction does not directly indicate the danger or soundness of individual facilities.
[Key points of this project] Converting on-site records into AI data. A system that goes beyond simply collecting data.
- Transforming dispersed inspection records into data that AI can learn from.
We organize drawings, ledgers, inspection photos, and repair history, and link them using location and structural information. - Synthetic data is used to supplement conditions that are difficult to collect using real data.
By reproducing changes in lighting and reflections on the water surface, it can be used for learning and evaluation in special environments. - We don't just collect data and stop there; we continue to update and provide it.
We will establish a system that connects data holders and developers, and delivers data that has been verified for use and quality.
(1) Business Overview | Connecting three types of data: location, state, and special environment
We will organize the format, items, terminology, quality, and usage conditions of on-site records, and prepare them in a state that can be used for AI learning and evaluation (AI-ready). The target data will be the following three groups:
AI ready data to be developed in this project
| dataset | Content to be represented and data to be compiled |
|---|---|
| A | Spatial Infrastructure | Where and what kind of structures are there? A reference base for location and shape across the entire country, and high-resolution 3D data for selected regions and sections. |
| B | Structural Infrastructure Inspection | What is the current situation? How was it handled in the past? Drawings, ledgers, inspection records, and repair histories are organized in a way that allows them to be traced for each facility and component. |
| C | Special Environment Learning | How to learn in an environment where it is difficult to acquire skills This data combines real-world data, such as the inside of sewer pipes and the underside of bridges, with data obtained through physical simulations. |
1) Use A as a foundation to connect locations, structures, and records.
Using A as a common reference axis, information B and C corresponding to facilities and sections will be linked by IDs that identify location and structure. For example, the goal is to be able to trace drawings, inspection photos, and repair history of the same bridge, not only for AI learning but also for inspection planning and verification of past actions.
The level of detail in the correlation varies depending on the source data, and not everything will be precisely positioned in small sections. The sewer survey footage will be linked to the pipe sections between manholes. Further details are provided in the attached document.
Please note that the nationwide network infrastructure is for common location reference purposes only and does not include detailed shape information or inspection data for all facilities across the country.
2) Synthetic data is used to supplement conditions that are difficult to obtain.
Synthetic data is not a substitute for real data. We aim to distinguish between information obtained from actual surveys and conditions reproduced through simulation, and to evaluate the effectiveness of learning using synthetic data with real data.
3) The roles of the three parties: Connecting research, data infrastructure, and the field.
By combining research expertise from Hosei University, spatial information integration technology from SSIL, and infrastructure practical experience and collaboration with local public organizations and administrators from Pacific Consultants, we will comprehensively advance everything from data preparation to continuous provision.
[Main roles]
* Hosei University: Data structuring, correlation with location and structures, design and technical supervision of quality evaluation.
* SSIL: Construction of a data infrastructure connecting spatial information, search, distribution, and system operation.
* Pacific Consultants: Coordination with local public organizations and infrastructure managers, collection of actual data, organization of rights and usage conditions, and data management.
(2) Expected effects
This project aims to reduce the burden of data search and organization on AI developers, and to promote the development of technologies for searching inspection records, extracting damage information, and recognition in special environments. By making facility information more accessible to local public organizations and infrastructure managers, it will support the development of inspection plans and preventive maintenance strategies.
Reduced inspection time and costs, and improved safety are expected benefits, but the actual effects require verification depending on the technology used and the application conditions. Performance and safety verification are necessary for the autonomous operation of AI and robots in real-world environments.
(3) Basic information on the selected projects
| Business name | Research and Development Project for Strengthening Post-5G Information and Communication System Infrastructure / Research and Development on the Construction of Data Ecosystems, etc. (GENIAC) |
|---|---|
| Selected theme name | Building AI-ready hybrid spatial and synthetic datasets to support improved infrastructure inspection efficiency and autonomous robot control. |
| Implementer | Hosei University / Space Services Innovation Lab Business Cooperative / Pacific Consultants Co., Ltd. |
| Date of announcement of selection results | July 2, 2026 |
| Implementation Period | Planned for two years, starting in September 2026. |
The project name, selected theme name, implementer, and publication date of the selection results are based on NEDO-published materials. The source is indicated in the attached document.
For further details, please refer to the PDF document.