Inspiration
Our passion for helping others inspired the creation of AFMAC, motivated by the recent fire disaster in Los Angeles. We identified the urgent need for smarter tools in firefighting and designed the Augmented Fire Management Center (AFMAC) to protect human life, wildlife, and ecosystems. AFMAC provides firefighters with innovative tools to save lives faster, make better decisions, and ensure their safety during critical moments.
What it Does AFMAC is an integrated intelligence system that enhances firefighting efficiency.
How Does It Work? AFMAC leverages artificial intelligence (AI), augmented reality (AR), and IoT technologies through three core features:
Smart Person Localization: A helmet-mounted camera allows real-time analysis of the firefighter's perspective. The system connects to databases and uses mobile geolocation to identify the precise location of individuals needing rescue. Tech used: Augmented Reality, Qualcomm Devices, IoT, GIS, GPS.
Heat Maps and Optimal Routes: Real-time heat maps created with GIS and augmented reality highlight critical areas. AI models calculate the safest, fastest rescue routes. Tech used: AI, GIS, AR, GPS.
Real-Time Coordination: A command center dashboard allows efficient monitoring and coordination of teams, optimizing unit deployment, reducing risks, and maximizing effectiveness. Tech used: AR, AI, IoT, GIS, Styly Tech.
How We Built It Our team of AI, data science, and engineering experts developed AFMAC with cutting-edge technology:
Hardware Development: Qualcomm devices host an AI-driven system capable of identifying individuals in danger using machine learning. Heat maps and spectrographic cameras help calculate the safest routes. Control Dashboard Integration:
A centralized dashboard powered by Lambda's AI and LLMs monitors firefighter units, vital signs, and real-time data. Simulation and Deployment: Simulated GPS data enables AFMAC to guide firefighters to individuals needing assistance.
Challenges We Faced Configuring advanced hardware. Overcoming access restrictions for specific technologies, such as Lambda AI.
Accomplishments We’re Proud Of Collaborating effectively as a diverse team. Developing a functional prototype in just three days. Transforming innovative ideas into practical solutions.
What We Learned The potential to turn visionary concepts into reality. The value of working with diverse, talented individuals.
What’s Next for AFMAC We aim to implement AFMAC in real-world firefighting scenarios, making it a lifesaving tool for emergency responders. Additionally, we see potential for AFMAC in other applications, such as snow rescue operations and disaster response.
Built With
- artificial-intelligence
- augmented-reality
- dash
- firebase
- lambda
- machine-learning
- python
- qualcomm
- styly
- tensorflow


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