Demonstration of Disaster Prevention DX for Recovery and Reconstruction in Suzu City
NTT West Hokuriku Branch and Suzu City conducted a disaster prevention DX demonstration project using NTT Fieldtechno's AI service, 'Audin AI.' By combining vehicle-mounted drive recorders and walking surveys to analyze evacuation routes and signs, the project achieved a 60% reduction in workload compared to conventional methods. They aim to expand this across the Noto region.
📋 Article Processing Timeline
- 📰 Published: May 19, 2026 at 02:28
- 🔍 Collected: May 18, 2026 at 18:01
- 🤖 AI Analyzed: May 18, 2026 at 18:29 (27 min after Collected)
NTT West Corporation Hokuriku Branch (Branch Manager: Kayoko Kosugi, hereinafter "NTT West Hokuriku Branch") conducted a field demonstration project utilizing "Audin AI" (an AI service that identifies roads, signs, etc., and inspects their deterioration status) *1, provided by NTT Fieldtechno Corporation. This was part of an initiative based on the "Partnership Agreement on the Realization of a 'Connected Society Created by Digital Technology' for Creative Reconstruction," concluded with Suzu City on November 27, 2025.
In this demonstration, targeting the survey of guidance signs installed along evacuation routes to designated emergency evacuation sites, and the preparation of ledgers of collected data (ledgering by lists and maps), a new operational method was verified. This new method combined traditional walking surveys with a technique that uses AI to analyze video captured by vehicle-mounted cameras to automatically organize information on evacuation routes and signs. As a result, an approximately 60% workload reduction effect was confirmed compared to the conventional method of walking surveys alone.
*1: A cloud service that combines drive recorder data with facility identification and deterioration diagnosis technology, standardizing inspection accuracy by AI and maintaining ledgers of social infrastructure to determine facility deterioration.
https://business.ntt-west.co.jp/solution/audin_ai
1. Background
Following the 2024 Noto Peninsula Earthquake and the Oku-Noto Heavy Rain, the advancement of disaster prevention and mitigation measures has become a critical issue in Suzu City.
Among these measures, surveying guidance signs installed on evacuation routes to designated emergency evacuation sites and preparing ledgers of the collected data are important in both normal and emergency times. However, these tasks required a significant amount of time and effort for local confirmation by staff.
To address these challenges, this demonstration project was implemented based on the partnership agreement, with the aim of exploring efficient survey and management methods utilizing AI.
2. Overview of the Demonstration Project
In this demonstration project, data collection and ledger preparation were carried out using the following methods.
(1) Target Area
40 evacuation routes to 15 designated emergency evacuation sites in the Horyu area of Suzu City.
(2) Implementation Details
[Areas Accessible by Vehicles]
NTT vehicles equipped with drive recorders were driven to acquire images and location information (Travel distance: approx. 30km)
[Areas Inaccessible by Vehicles]
On-site confirmation was conducted through walking surveys.
Regarding the collected data, by combining AI judgment by Audin AI and visual judgment, the installation locations of guidance signs were extracted, matched with the hazard map data held by Suzu City, and the types and installation locations of the guidance signs were organized into a ledger (listed and mapped).
(3) Role Allocation
NTT West Hokuriku Branch: Project promotion for advancing disaster prevention operations
NTT Fieldtechno: Technical support for Audin AI, field surveys, and service provision
Suzu City: Provision of the demonstration field
3. Effects of the Demonstration Project
Compared to conventional on-site surveys by staff, this demonstration project confirmed a workload reduction effect of approximately 60% by combining vehicle surveys using Audin AI, walking surveys, and image analysis.
Through this, in addition to reducing the burden of movement, confirmation, and organization tasks on staff, it is expected that operational efficiency and the utility of information will be improved by listing and mapping the survey results.
Furthermore, anticipating that the decline in the labor force population will make it difficult to maintain operations with conventional methods alone in the future, it was confirmed that this is effective as a new operational method that achieves both labor savings and operational leveling.
4. Future Development
NTT West Hokuriku Branch will share the knowledge gained from this demonstration project with Suzu City and promote DX in the disaster prevention field, aiming to build a model for advanced disaster prevention operations in the city.
At the same time, we will expand this initiative across the entire Noto region, contributing to solving regional issues utilizing AI and the creative reconstruction of the Noto region through region-wide implementation.
*The information contained in the news release is current as of the date of the announcement. Please note in advance that it is subject to change.
In this demonstration, targeting the survey of guidance signs installed along evacuation routes to designated emergency evacuation sites, and the preparation of ledgers of collected data (ledgering by lists and maps), a new operational method was verified. This new method combined traditional walking surveys with a technique that uses AI to analyze video captured by vehicle-mounted cameras to automatically organize information on evacuation routes and signs. As a result, an approximately 60% workload reduction effect was confirmed compared to the conventional method of walking surveys alone.
*1: A cloud service that combines drive recorder data with facility identification and deterioration diagnosis technology, standardizing inspection accuracy by AI and maintaining ledgers of social infrastructure to determine facility deterioration.
https://business.ntt-west.co.jp/solution/audin_ai
1. Background
Following the 2024 Noto Peninsula Earthquake and the Oku-Noto Heavy Rain, the advancement of disaster prevention and mitigation measures has become a critical issue in Suzu City.
Among these measures, surveying guidance signs installed on evacuation routes to designated emergency evacuation sites and preparing ledgers of the collected data are important in both normal and emergency times. However, these tasks required a significant amount of time and effort for local confirmation by staff.
To address these challenges, this demonstration project was implemented based on the partnership agreement, with the aim of exploring efficient survey and management methods utilizing AI.
2. Overview of the Demonstration Project
In this demonstration project, data collection and ledger preparation were carried out using the following methods.
(1) Target Area
40 evacuation routes to 15 designated emergency evacuation sites in the Horyu area of Suzu City.
(2) Implementation Details
[Areas Accessible by Vehicles]
NTT vehicles equipped with drive recorders were driven to acquire images and location information (Travel distance: approx. 30km)
[Areas Inaccessible by Vehicles]
On-site confirmation was conducted through walking surveys.
Regarding the collected data, by combining AI judgment by Audin AI and visual judgment, the installation locations of guidance signs were extracted, matched with the hazard map data held by Suzu City, and the types and installation locations of the guidance signs were organized into a ledger (listed and mapped).
(3) Role Allocation
NTT West Hokuriku Branch: Project promotion for advancing disaster prevention operations
NTT Fieldtechno: Technical support for Audin AI, field surveys, and service provision
Suzu City: Provision of the demonstration field
3. Effects of the Demonstration Project
Compared to conventional on-site surveys by staff, this demonstration project confirmed a workload reduction effect of approximately 60% by combining vehicle surveys using Audin AI, walking surveys, and image analysis.
Through this, in addition to reducing the burden of movement, confirmation, and organization tasks on staff, it is expected that operational efficiency and the utility of information will be improved by listing and mapping the survey results.
Furthermore, anticipating that the decline in the labor force population will make it difficult to maintain operations with conventional methods alone in the future, it was confirmed that this is effective as a new operational method that achieves both labor savings and operational leveling.
4. Future Development
NTT West Hokuriku Branch will share the knowledge gained from this demonstration project with Suzu City and promote DX in the disaster prevention field, aiming to build a model for advanced disaster prevention operations in the city.
At the same time, we will expand this initiative across the entire Noto region, contributing to solving regional issues utilizing AI and the creative reconstruction of the Noto region through region-wide implementation.
*The information contained in the news release is current as of the date of the announcement. Please note in advance that it is subject to change.
FAQ
What technology was used in Suzu City's disaster prevention DX demonstration?
The AI service 'Audin AI' provided by NTT Fieldtechno, which inspects infrastructure by analyzing drive recorder footage.
What were the results of the demonstration?
A workload reduction of approximately 60% was confirmed in surveying evacuation route signs compared to traditional walking surveys.
What are the future plans?
The plan is to build a model for advanced disaster prevention operations in Suzu City and expand it across the entire Noto region.