AI-Driven Vegetation Monitoring and Fire Risk Mitigation for Wildfire Prevention and Powerline Safety

Ensuring the safety and reliability of power lines is crucial for preventing forest fires caused by vegetation encroachment. Traditional monitoring methods are labor-intensive, costly, and error-prone, often resulting in delayed identification of potential hazards. ForestGuard, an AI-powered vegetation monitoring and fire risk management system, addresses these challenges by integrating advanced geospatial and machine learning technologies. The system uses high-resolution satellite imagery and YOLOv5 for near-real-time detection and classification of vegetation and potential hazards around power lines. Built with Python and QGIS, ForestGuard automates the creation of Digital Terrain Models (DTM) and Digital Surface Models (DSM) to assess vegetation height and proximity to power lines. Upon identifying hazardous conditions, the system promptly notifies relevant authorities, enabling rapid responses to mitigate risks. By reducing reliance on manual inspections and ensuring timely interventions, ForestGuard minimizes the likelihood of power outages and wildfires. This scalable and efficient solution demonstrates significant potential for enhancing proactive vegetation management and improving the safety and reliability of power line networks.