Situational Awareness System
The Situational Awareness System (SAS) is a real-time, AI-powered platform designed to detect, classify, and visualize local events such as crimes, disasters, and social incidents using social media data. It combines advanced natural language processing (NLP), deep learning, and geospatial analysis to support timely decision-making.
Why SAS?
In today's digital age, platforms like Twitter and Telegram are often the first places where users share urgent events. However, the massive flow of information makes it difficult to manually track and analyze these incidents.
SAS solves this problem by:
- Automatically detecting events from social media streams.
- Classifying events by type (crime, accident, disaster, etc.).
- Inferring geographic locations from text, even when no coordinates are provided.
- Visualizing events on an interactive map with real-time updates.
Key Features
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🧠 AI-Powered Event Detection
Uses deep learning models and Named Entity Recognition (NER) to extract meaningful insights from raw text. -
🗺️ Interactive Awareness Map
View live and historical events with geolocation context. -
🔔 Region-Based User Notifications
Users can subscribe to geographic areas and receive alerts for relevant incidents. -
📊 Dashboards and Reports
Generate analytical insights, event timelines, and visual statistics.
Target Users
- Emergency response teams and disaster managers
- Journalists and researchers
- Local authorities and city planners
- Citizens and community members
Platform Architecture
SAS is built with a microservices architecture using modern backend and frontend technologies. It includes components for:
- Data scraping from social media (Twitter, Telegram)
- Event detection and location inference
- User identity and access control
- Notifications and preference management
- Web UI and API gateway
In the next sections, you’ll find detailed documentation for each of these components, their roles, APIs, and deployment steps.