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This research proposes “ForCM”, a novel approach to forest cover mapping that combines Object-Based Image Analysis (OBIA) with Deep Learning (DL) using multispectral Sentinel-2 imagery. The study initially explores the application of several DL models such as UNet, UNet++, ResUNet, AttentionUNet, and ResNet50-Segnet–on high-resolution Sentinel-2 Level 2A satellite images of the Amazon Rainforest. The datasets comprise three primary collections: two sets of 3-band imagery and one set of 4-band imagery. After evaluating the DL models, the most effective ones are individually integrated with the OBIA technique to enhance mapping accuracy. The originality of this work lies in the evaluation of different deep learning models combined with OBIA, and their comparison with traditional OBIA methods. The findings indicate that the proposed “ForCM” method significantly improves forest cover mapping, achieving overall accuracies of 94.54% with ResUNet-OBIA and 95.64% with AttentionUNet-OBIA, compared to 92.91% with the traditional OBIA approach. Furthermore, this research demonstrates the potential of free and user-friendly tools like QGIS for achieving precise mapping within their limitations, supporting global environmental monitoring and conservation efforts.

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ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Maisha Haque,
  • Israt Jahan Ayshi,
  • Sadaf M. Anis,
  • Nahian Tasnim,
  • Mithila Moontaha,
  • Md. Sabbir Ahmed,
  • Muhammad Iqbal Hossain,
  • Mohammad Zavid Parvez,
  • Subrata Chakraborty,
  • Biswajeet Pradhan,
  • Biswajit Banik

摘要

This research proposes “ForCM”, a novel approach to forest cover mapping that combines Object-Based Image Analysis (OBIA) with Deep Learning (DL) using multispectral Sentinel-2 imagery. The study initially explores the application of several DL models such as UNet, UNet++, ResUNet, AttentionUNet, and ResNet50-Segnet–on high-resolution Sentinel-2 Level 2A satellite images of the Amazon Rainforest. The datasets comprise three primary collections: two sets of 3-band imagery and one set of 4-band imagery. After evaluating the DL models, the most effective ones are individually integrated with the OBIA technique to enhance mapping accuracy. The originality of this work lies in the evaluation of different deep learning models combined with OBIA, and their comparison with traditional OBIA methods. The findings indicate that the proposed “ForCM” method significantly improves forest cover mapping, achieving overall accuracies of 94.54% with ResUNet-OBIA and 95.64% with AttentionUNet-OBIA, compared to 92.91% with the traditional OBIA approach. Furthermore, this research demonstrates the potential of free and user-friendly tools like QGIS for achieving precise mapping within their limitations, supporting global environmental monitoring and conservation efforts.