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Domain adaptive deep learning framework designed particularly for the purpose of forest cover type deforestation in the Western Ghats, Tamil Nadu, India, using weakly supervised learning coupled with domain adaptation techniques applied to exploit the multi-temporal satellite imagery for understanding specific environmental issues in a biodiversity hotspot. It addresses the issue of variability in data caused by different ecological zones within the Western Ghats through the application of a DANN. The model will use the integration of U-Net for image segmentation and transformer-based models for sequential data processing. Preliminary results have shown considerable improvements regarding detection accuracy and generalization. This offers a real-time monitoring tool to support conservation efforts, thus working toward the achievement of SDG 13: Climate Action and SDG 15: Life on Land. The satellite imagery for this research is derived from advanced Earth observation platforms, including SPOT (Satellite Pour observation de la Terre) and WorldView series, which deliver high-resolution data appropriate for detailed environmental monitoring.

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Deforestation Detection Using Domain Adversarial Neural Network

  • 会议论文

  • 发表日期：

    2025年

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  • Kasi Viswanathan Kannan,
  • M. Karthikeyan,
  • R. V. Elatchuman,
  • G. K. Sandhia,
  • S. Girirajan

摘要

Domain adaptive deep learning framework designed particularly for the purpose of forest cover type deforestation in the Western Ghats, Tamil Nadu, India, using weakly supervised learning coupled with domain adaptation techniques applied to exploit the multi-temporal satellite imagery for understanding specific environmental issues in a biodiversity hotspot. It addresses the issue of variability in data caused by different ecological zones within the Western Ghats through the application of a DANN. The model will use the integration of U-Net for image segmentation and transformer-based models for sequential data processing. Preliminary results have shown considerable improvements regarding detection accuracy and generalization. This offers a real-time monitoring tool to support conservation efforts, thus working toward the achievement of SDG 13: Climate Action and SDG 15: Life on Land. The satellite imagery for this research is derived from advanced Earth observation platforms, including SPOT (Satellite Pour observation de la Terre) and WorldView series, which deliver high-resolution data appropriate for detailed environmental monitoring.