Satellite Image-Based Tree Enumeration and Sustainable Route Planning for Forest Land Diversion

Forest land diversion requires accurate tree enumeration and environmentally sustainable planning to minimize ecological impact. Traditional manual survey methods are time-consuming, costly, and prone to inaccuracies, especially in large forest regions. This project proposes an automated system using satellite imagery and deep learning techniques for efficient tree detection and counting. A YOLOv8-based object detection model is employed to identify individual trees and generate bounding boxes with confidence scores. Tree density analysis is performed to identify ecologically sensitive areas. Based on this analysis, optimized routes are generated using shortest path algorithms to reduce environmental disturbance. The proposed system ensures scalability, improved accuracy, and supports data-driven decision-making for sustainable forest management