Abstract

The diversion of forest land for development requires accurate tree enumeration to assess environmental impact. Traditional methods, like manual counting and sampling, are labor-intensive, time-consuming, and prone to error. This project leverages high-resolution satellite and drone imagery, combined with advanced image processing and machine learning, to automate tree counting. Our system includes analytical tools, and provides authorities with historical environmental data (like rainfall, temperature, humidity) for informed decision-making. With a userfriendly interface and appealing data visualizations, it also integrates Google Maps API for user-generated satellite imagery. This solution aims to enhance accuracy, efficiency, and scalability in tree enumeration, supporting sustainable forest management and regulatory compliance.