AI Disaster Response Assistant

Inspiration

Natural disasters such as floods, earthquakes, and hurricanes often leave emergency teams overwhelmed with fragmented information. Rescue teams must quickly analyze images, messages, and reports to identify where help is needed the most. Unfortunately, this process is usually manual and slow.

We were inspired by the idea that artificial intelligence could drastically reduce response time by automatically analyzing disaster data. Our goal was to create an AI-powered system that helps emergency responders prioritize rescue efforts using real-time information from images and text reports.

By leveraging Amazon Nova models, we wanted to demonstrate how multimodal AI can assist decision-making in high-pressure emergency situations.

What it does

AI Disaster Response Assistant is an intelligent platform that analyzes disaster-related data and converts it into actionable rescue insights.

The system accepts multiple types of inputs:

• Drone images
• Satellite photos
• Ground-level images from citizens
• Text reports or emergency messages

The AI then processes this information and identifies:

• Flooded regions
• Damaged buildings
• Blocked roads
• Potential survivors
• High-priority rescue zones

The platform visualizes the results on a disaster map and generates prioritized rescue recommendations for emergency teams.

How we built it

The system was built using a cloud-based architecture designed for scalability and real-time analysis.

AI Layer

We used Amazon Nova through Amazon Bedrock for multimodal reasoning. The model analyzes uploaded images and extracts structured information such as infrastructure damage, flood severity, and visible survivors.

Built With

• amazon-bedrock
• amazon-nova
• amazon-web-services
• backend
• css
• express.js
• git
• github
• html5
• javascript
• mapbox
• node.js
• postman
• python
• react
• s3