— About
Nadir GHEZALI
Embedded Systems Engineer
The project
NumidAI is an independent research and development project focused on autonomous wildfire detection at the edge.
This project is personal. Wildfires cause devastating damage every year across the world — including in the region my parents are from, where lives have been lost and vast landscapes of olive groves have turned to ash.
Early detection is crucial in wildfire management. Catching a wildfire in its first minutes can mean the difference between containing a small blaze and losing large areas of vegetation and potentially lives.
Background
Experience
20+ years in embedded systems and real-time software. C++ and Linux specialist. Experience in video processing, edge computing, and software architecture.
Why NumidAI
NumidAI is the concrete application of embedded systems expertise to a problem that matters: early wildfire detection.
I have worked on every single aspect of the system — from initial specifications and requirements, through data engineering, model training and evaluation, to embedded integration and optimization on edge hardware.
Data — Dataset aggregation and curation at scale. Metadata taxonomy design. Hard negative mining. Model-assisted labeling. Stratified splitting with strict data leakage prevention.
Model — Recall-first detection strategy. Signal-aware training distribution. Threshold calibration under operational false-positive constraints.
Embedded software — Real-time C++ inference pipeline on edge hardware. Multi-threaded architecture with sub-100ms end-to-end latency. Multi-layer detection and semantic filtering.
Everything else — System architecture, deployment strategy, field evaluation protocol, technical documentation, and this website.
A project built to validate a technical hypothesis on real hardware, using real data, with the ambition of turning an embedded AI demonstrator into a robust real-world system.
Open to
- Field validation partnerships with fire services and land management agencies
- Technical collaboration in edge AI for environmental monitoring
- Feedback from practitioners working with wildfire camera networks