Hierarchical Convolution Neural Network for Deforestation Risk Evaluation

Deforestation is a major danger to biodiversity, climate stability, and ecosystems. Accurately measuring and predicting the hazards associated with deforestation is critical for effective conservation and policymaking. This paper introduces a novel way to assess deforestation risk using Hierarchical Convolutional Neural Networks (HCNNs) driven by Artificial Intelligence (AI). The proposed model analyzes satellite imagery and environmental data hierarchically, learning geographical patterns and risk variables connected with deforestation activities. By including multi-scale feature extraction, the hierarchical CNN model yields strong predictions and risk assessments at various levels of detail, encompassing both macro and micro environmental changes. The model is trained and verified using real-world deforestation datasets, achieving an excellent accuracy of 98.9%, precision of 97.3%, recall of 96.0%, and F1-score of 96.6%, outperforming traditional machine learning (ML) methods.