Pakistan's disaster alerts are trapped in PDFs, buried in social media, and fragmented across agencies. Meanwhile, millions wait for warnings that arrive too late—or never reach them at all. REACH uses AI to transform this chaos into clarity, automatically collecting and processing emergency alerts into location-specific warnings that actually save lives.
The 2025 Pakistan floods impacted 6.9 million people and resulted in 1,037 fatalities. Despite having early warning systems, communities continue to face devastating losses because:
- 📄 Critical alerts are buried in verbose PDFs that take hours to parse
- 🗺️ Warnings lack location specificity - entire provinces get alerted when only specific districts are at risk
- ⏱️ Information travels too slowly through bureaucratic hierarchies
- 🔍 No unified view of threats across NDMA, PDMAs, PMD, and other sources
- 📱 Digital infrastructure is underutilized despite 79% mobile penetration
The gap between institutional forecasting and community-level preparedness is costing lives.
REACH bridges this gap with AI-powered alert intelligence. Our system:
🔄 Automatically scrapes official sources (NDMA, NEOC, PMD, PDMAs)
🧠 Transforms chaos into clarity using Vision-Language Models and LLMs
🗺️ Maps threats precisely with intelligent geocoding
📲 Delivers actionable alerts through a real-time dashboard
Multimodal AI Processing 🎯
Unlike text-only systems, REACH uses Vision-Language Models to extract critical information from weather maps, infographics, and scanned bulletins that human operators would need hours to interpret.
Intelligent Normalization 📝
Advanced LLM reasoning transforms verbose reports like "moderate to heavy rainfall expected in upper catchments" into clear guidance: "Flash flood risk in Swat River areas - evacuate low-lying areas immediately."
CAP-Standard Structuring 📊
All alerts are normalized to Common Alerting Protocol-inspired JSON format, making them machine-readable and integration-ready for future notification systems.
🎬 Watch Full Demo Video (add link)
Our hackathon submission demonstrates the core intelligence layer:
- Monitors NDMA situation reports, NEOC updates, and PMD bulletins
- Handles PDFs, HTML pages, and social media posts
- Resilient parsing that adapts to format changes
- Vision-Language Models extract data from weather maps and infographics
- Reasoning LLMs understand context and severity
- Geocoding engine translates vague locations ("areas downstream of Tarbela Dam") into precise coordinates
- Interactive map visualization of active threats
- Filterable by region, severity, and alert type
- Historical alert database for pattern analysis
{
"identifier": "NDMA-2025-FLOOD-001",
"severity": "Extreme",
"event": "Flash Flood",
"headline": "Immediate evacuation required for Swat River areas",
"areas": [
{
"name": "Swat District",
"geocode": [35.2227, 72.4258]
}
],
"instruction": "Evacuate low-lying areas immediately. Move to higher ground.",
"expires": "2025-01-04T18:00:00+05:00"
}- Qwen3-VL - Vision-Language model for document understanding
- DeepSeek R1 - Reasoning and alert normalization
- olmOCR 2 - Text extraction from images
- Novita AI / Deepinfra - Inference providers
- Scrapy + Playwright - Robust web scraping
- RabbitMQ - Message queue for processing pipeline
- PostgreSQL + PostGIS - Geospatial database
- Supabase - Real-time database and auth
- React.js - Dashboard interface
- Apache ECharts - Data visualization
- Mapbox - Interactive mapping
- Digital Ocean - Hosting
- Netlify - Frontend deployment
- Docker - Containerization
┌─────────────┐
│ Sources │ NDMA, NEOC, PMD, PDMAs
└──────┬──────┘
│
↓
┌─────────────┐
│ Scrapers │ Playwright, Scrapy
└──────┬──────┘
│
↓
┌─────────────┐
│RabbitMQ Queue│
└──────┬──────┘
│
↓
┌─────────────┐
│ AI Pipeline │ VLMs + LLMs
│ • Extract │ (Qwen3-VL, DeepSeek R1)
│ • Reason │
│ • Geocode │
└──────┬──────┘
│
↓
┌─────────────┐
│ Database │ PostgreSQL + PostGIS
└──────┬──────┘
│
↓
┌─────────────┐
│ Dashboard │ React + Mapbox
└─────────────┘
- ⚡ 10-minute alert delivery vs current 2-6 hour delays
- 🎯 District-level precision vs province-wide warnings
- 📖 Plain-language guidance vs technical jargon
- 🌐 Single unified view of all official sources
According to UN research, comprehensive multi-hazard early warning systems reduce disaster-related fatality rates by 82.5% (from 4.05 to 0.71 per 100,000 population). REACH's intelligence layer is the critical foundation for achieving this in Pakistan.
Phase 1: Intelligence ✅ (Hackathon Prototype)
Core AI pipeline and dashboard
Phase 2: Distribution 🚧
Push notifications, SMS integration, mobile apps
Phase 3: Community 📋
Verified user submissions, field reports
Phase 4: Scale 🌍
Multi-language, regional expansion, API access
This project was built for [Hackathon Name] by a team that believes technology should serve humanity's most pressing needs. Every line of code represents our commitment to protecting communities from climate disasters.
[Your Name] - AI/ML Engineer
[Team Member 2] - Full Stack Developer
[Team Member 3] - Data Engineer
[Team Member 4] - UX/UI Designer
We're actively seeking partnerships with:
- 🏛️ Government agencies (NDMA, PDMAs)
- 🌍 International development organizations
- 📱 Telecom providers for SMS integration
- 🎓 Research institutions
Email: reach.contact@example.com
Twitter: @ReachAlerts
Website: reach-alerts.org
- WWF Pakistan for problem validation and research support
- NDMA, NEOC, and PMD for their tireless work in disaster monitoring
- The open-source community for incredible tools and libraries
- Communities affected by the 2025 floods - this is for you