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Developing AI powered platforms for Automated Deforestation Monitoring

  • Unique Paper ID: 172531
  • Volume: 11
  • Issue: 9
  • PageNo: 559-562
  • Abstract:
  • Deforestation is a critical environmental issue that contributes to biodiversity loss, climate change, and ecosystem degradation. Traditional methods of monitoring deforestation are time-consuming, costly, and often limited in scope. This study proposes the development of an AI-powered platform for automated deforestation monitoring, leveraging advanced machine learning (ML) and deep learning (DL) techniques to enhance accuracy, speed, and scalability in detecting and analyzing forest cover changes. The platform utilizes satellite imagery, remote sensing data, and real-time environmental inputs to automatically identify and classify deforestation events across vast areas. By integrating convolutional neural networks (CNNs) and other image recognition algorithms, the system is designed to detect subtle changes in forest cover, such as illegal logging and forest degradation, with minimal human intervention. Furthermore, the platform incorporates predictive analytics to forecast potential future deforestation hotspots, enabling proactive intervention strategies. The proposed solution aims to provide governments, conservation organizations, and researchers with a powerful tool to monitor deforestation on a global scale, offering timely insights for informed decision-making and the promotion of sustainable land management practices. This research presents a step forward in combining AI technology with environmental protection efforts, contributing to more efficient, scalable, and accurate monitoring of one of the most pressing ecological challenges of our time.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 9
  • PageNo: 559-562

Developing AI powered platforms for Automated Deforestation Monitoring

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