Satellite Image-Based Deforestation Monitoring Using Convolutional Neural Networks
Keywords:
Deforestation Detection, Satellite Image Segmentation, Deep Learning, Convolutional Neural Network (CNN), U-Net Architecture, Remote Sensing, Semantic Segmentation, Environmental Monitoring, TensorFlow, Satellite Imagery Analysis.Abstract
Deforestation is a serious environmental problem that calls for precise and automated monitoring technologies to promote sustainable forest management and stop illicit logging. This study proposes a deep learning-based satellite image segmentation framework using a Convolutional Neural Network (CNN) for deforestation detection. To categorize satellite imagery into forested, deforested, and non-forest land cover, a U-Net-based semantic segmentation model is utilized. To ensure effective data handling, a bespoke dataset made up of high-resolution satellite photos and the pixel-level segmentation masks that go with them was created and preprocessed using TensorFlow TFRecord pipelines. To increase training efficiency and scalability, TensorFlow with TPU acceleration was used to train the model. According to experimental findings, the suggested method successfully learns contextual and spatial characteristics related to patterns of woodland loss, allowing for accurate segmentation of impacted areas. To encourage reproducibility and make additional study easier, the trained model and dataset are made publicly available. This method demonstrates how deep learning-driven image segmentation can be a dependable instrument for automated environmental monitoring and deforestation analysis
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