Launch of "AI DisasterRecovery on IDX" to Innovate Disaster Recovery and National Resilience, Addressing 17 Government Priority Areas!
AI Data Inc. has launched "AI DisasterRecovery on IDX," a generative AI platform specialized in disaster recovery. It streamlines initial disaster response and report generation by integrating massive amounts of disaster prevention data.
📋 Article Processing Timeline
- 📰 Published: April 7, 2026 at 20:30
- 🔍 Collected: April 7, 2026 at 12:00
- 🤖 AI Analyzed: April 20, 2026 at 22:20 (322h 20m after Collected)
AI Data Co., Ltd. (Headquarters: Minato-ku, Tokyo; President and CEO: Takahito Sasaki; hereinafter "AI Data"), a company utilizing enterprise data and AI, will begin offering "AI DisasterRecovery on IDX," an integrated knowledge platform utilizing generative AI specialized in disaster recovery, disaster mitigation, and land management in the "Disaster Prevention and National Resilience" domain, which is among the 17 priority areas set by the Japanese government.
AI DisasterRecovery on IDX is an industry-specific AI platform that integrates and structures vast amounts of disaster prevention data and knowledge across multiple fields, such as weather, rivers, earthquakes, landslides, infrastructure inspections, evacuation centers, and local government plans. It comprehensively supports the innovation of initial response speed in the event of a disaster, the immediate generation of recovery plans, and the standardization of responses among local governments.
▼ Disaster Recovery Specialized AI Platform AI DisasterRecovery on IDX
■ Background: Structural Issues in Initial Response and Land Management in Disaster-Prone Japan
Japan is a country with exceptionally high risks of natural disasters globally, including earthquakes, typhoons, heavy rains, floods, and landslides. However, the biggest challenge when a disaster occurs lies in the "slowness of the initial response."
Digitalization and response standardization are positioned as important issues by the government's Basic Plan for National Resilience and disaster prevention DX promotion measures.
On the other hand, the following issues are becoming more serious on the ground:
- It takes days to weeks to confirm the damage situation, significantly delaying recovery.
- Due to a shortage of personnel for on-site investigations and difficulties in entering dangerous areas, the initial response is dependent on individual skills.
- The response format differs for each local government, delaying coordination with the national government and other agencies.
- Disaster response know-how depends on veteran staff, leading to a decline in disaster prevention capabilities due to retirements and transfers.
- It is difficult to grasp the overall picture of aging infrastructure, making it difficult to optimize investment priorities.
- Information on weather, rivers, earthquakes, evacuation centers, infrastructure, etc., is divided by organization, preventing integrated decision-making.
AI Data developed AI DisasterRecovery on IDX to solve these structural issues.
■ Main Features of AI DisasterRecovery on IDX
1. Damage Diagnosis Support AI
Quickly organizes on-site information and assists in judgment
- Tags, classifies, and instantly searches images captured by CCTVs and drones.
- Matches against past disaster data using a RAG foundation and presents similar cases and response examples.
- Centrally manages information gathered from the field, organizing it to easily grasp the overall picture of the damage situation.
2. Damage Report Generation AI
Significantly reduces the time required to create reports
- Automatically summarizes and analyzes text data collected in on-site investigations.
- Instantly generates a draft of the damage situation report based on the collected data.
AI DisasterRecovery on IDX is an industry-specific AI platform that integrates and structures vast amounts of disaster prevention data and knowledge across multiple fields, such as weather, rivers, earthquakes, landslides, infrastructure inspections, evacuation centers, and local government plans. It comprehensively supports the innovation of initial response speed in the event of a disaster, the immediate generation of recovery plans, and the standardization of responses among local governments.
▼ Disaster Recovery Specialized AI Platform AI DisasterRecovery on IDX
■ Background: Structural Issues in Initial Response and Land Management in Disaster-Prone Japan
Japan is a country with exceptionally high risks of natural disasters globally, including earthquakes, typhoons, heavy rains, floods, and landslides. However, the biggest challenge when a disaster occurs lies in the "slowness of the initial response."
Digitalization and response standardization are positioned as important issues by the government's Basic Plan for National Resilience and disaster prevention DX promotion measures.
On the other hand, the following issues are becoming more serious on the ground:
- It takes days to weeks to confirm the damage situation, significantly delaying recovery.
- Due to a shortage of personnel for on-site investigations and difficulties in entering dangerous areas, the initial response is dependent on individual skills.
- The response format differs for each local government, delaying coordination with the national government and other agencies.
- Disaster response know-how depends on veteran staff, leading to a decline in disaster prevention capabilities due to retirements and transfers.
- It is difficult to grasp the overall picture of aging infrastructure, making it difficult to optimize investment priorities.
- Information on weather, rivers, earthquakes, evacuation centers, infrastructure, etc., is divided by organization, preventing integrated decision-making.
AI Data developed AI DisasterRecovery on IDX to solve these structural issues.
■ Main Features of AI DisasterRecovery on IDX
1. Damage Diagnosis Support AI
Quickly organizes on-site information and assists in judgment
- Tags, classifies, and instantly searches images captured by CCTVs and drones.
- Matches against past disaster data using a RAG foundation and presents similar cases and response examples.
- Centrally manages information gathered from the field, organizing it to easily grasp the overall picture of the damage situation.
2. Damage Report Generation AI
Significantly reduces the time required to create reports
- Automatically summarizes and analyzes text data collected in on-site investigations.
- Instantly generates a draft of the damage situation report based on the collected data.