Deep Learning for Deforestation Monitoring and Sustainable Forest Management
Abstract
Deforestation poses a significant threat to biodiversity and climate stability. This paper examines deep learning approaches for monitoring forest cover, detecting illegal logging activities, and supporting reforestation initiatives. Convolutional neural networks (CNNs) and satellite image processing techniques are used to analyze deforestation patterns and predict areas at risk. We also explore reinforcement learning models for optimizing forest conservation strategies. Case studies from organizations leveraging AI for forest sustainability highlight the effectiveness of machine learning in preserving ecosystems and combating climate change.
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