Comprehensive Image Analytics for Diversion of Forest Land

This research study presents an initiative that seeks to revolutionize the process of tree enumeration and categorization, traditionally carried out manually. It harnesses the power of image analytics, the system aims to streamline the process, reduces manpower requirements, and lowers costs, all while minimizing the environmental impact of development projects. By incorporating techniques such as Binary Image Thresholding and Convolutional Neural Networks, the system categorizes and monitors trees using high-resolution satellite imagery. Analytical tools, including YOLOv8 for object detection and NDVI analysis for vegetation classification, are deployed to optimize land allocation and pathfinding. The proposed system's design is centered around efficiency and environmental consciousness. An interactive user interface is developed ensuring accessibility and ease of use. The integration of Google Maps API enables real-time monitoring of forest changes, supporting effective decision-making for deforestation and reforestation efforts.