Abstract

Accurate land cover mapping is crucial for various applications, from environmental monitoring to urban planning. Traditional methods often struggle with high-dimensional data and complex landscape features. This study integrates RCNN (Region-based Convolutional Neural Network) and ANT Colony Optimization (ACO) to enhance land cover mapping accuracy. RCNN is utilized for precise segmentation of high-resolution satellite imagery, while ACO is employed for effective feature extraction, leveraging the algorithm's ability to identify and optimize features in the presence of complex patterns. Our method was evaluated using a dataset of 500 km², achieving a segmentation accuracy of 92.5% and a feature extraction precision improvement of 18.3% compared to conventional techniques. The integration of RCNN and ACO demonstrates significant advancements in capturing detailed land cover information and improving overall mapping accuracy.