Optimized Deep Learning Approach For Enhanced Detection Of Deforestation

Study introduces an innovative deep learning concept for improved deforestation recognition. Monitoring is crucial as the climate and the depleted forests significantly impact the environment. Due to high false positive rates and limited resolution, they can be a drawback to the conventional approaches based on satellite images and using traditional machine learning techniques. To enhance the accuracy of the features and classification, the suggested approach implements a hybrid model, which is named Greylag Goose Optimization – mutated Versatile Convolutional Neural Network (GGO-VCNN). To improve the performance and eliminate processing inefficiencies, the high resolution satellite images are applied with the customized neural network framework. Compared with existing methods, our proposed method achieved higher values in accuracy (94.5%), precision (95.7%), recall (98.4%), and f1-score (97.1%). The experimental results show that the improved model performs better than the current methods, providing more accurate and reliable deforestation detection.