H10 : How accurately can an autonomous drone classify vegetation fuel types and generate flammability maps?
| Students | Arnav Kudale
Prathamesh Perlawar |
| School | HDSB - White Oaks Secondary School - Oakville |
| Level | Senior 11/12 - Grade 11 |
| Group | Group 8 - Engineering and Computing II |
| Abstract | Wildfires are increasing, yet current vegetation fuel assessments using manual surveys or satellite imagery are costly and low in resolution. This project investigates whether an autonomous drone-based computer vision system can more accurately classify forest fuel types and generate flammability heatmaps. A custom-built drone captured aerial images, which were stitched and analyzed using a trained machine learning model to classify vegetation. The system produced meter-level resolution heatmaps identifying high-risk fuel zones. Results showed significantly higher spatial detail and faster data collection compared to conventional methods, demonstrating a scalable and cost-effective solution for wildfire risk monitoring. |