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<p>Conventional expert interpretation remains the principal approach for geomorphological mapping. However, its application over large and heterogeneous areas can be time-consuming and influenced by interpretative variability, particularly in complex fluvial landscapes. This study develops a convolutional neural network (CNN)-based workflow to support geomorphological mapping in Northern Thailand using a multispectral and terrain input stack. The model was trained using an expert-interpreted reference geomorphology map and twelve geospatial layers derived from Landsat 8–9 Operational Land Imager imagery and Shuttle Radar Topography Mission data, including multispectral bands, normalised difference vegetation index, elevation, slope, curvature, topographic position index, and topographic wetness index. A High-Resolution Boundary U-Net (HRBoundaryUNet) model was implemented and compared with selected benchmark segmentation models using mean intersection over union (mIoU) and macro F1-score. To reduce spatial autocorrelation and patch-overlap leakage, model evaluation was performed using a spatially buffered stratified validation scheme, with performance further assessed using per-class intersection over union (IoU) and F1-score. Spatial discrepancy analysis was also applied to distinguish direct agreement, boundary generalisation, and spatial misclassification between the CNN-enhanced map and the reference map. Field investigations from unmanned aerial vehicle surveys and soil coring were used as point-based supporting evidence to corroborate geomorphological interpretation. The results demonstrate that CNN-based segmentation can serve as a reproducible and semi-automated mapping support tool for regional geomorphological mapping. The proposed workflow complements expert-based mapping by providing quantitative spatial evaluation, improving consistency in map generation, and offering a transparent basis for subsequent expert interpretation.</p>

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Enhancing Geomorphological Mapping in Northern Thailand Using Convolutional Neural Networks

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Ahmad Arif Apriadi,
  • Pakawadee Anussornrajkit,
  • Weeradetch Tanapalungkorn,
  • Veerayut Komolvilas,
  • Suched Likitlersuang

摘要

Conventional expert interpretation remains the principal approach for geomorphological mapping. However, its application over large and heterogeneous areas can be time-consuming and influenced by interpretative variability, particularly in complex fluvial landscapes. This study develops a convolutional neural network (CNN)-based workflow to support geomorphological mapping in Northern Thailand using a multispectral and terrain input stack. The model was trained using an expert-interpreted reference geomorphology map and twelve geospatial layers derived from Landsat 8–9 Operational Land Imager imagery and Shuttle Radar Topography Mission data, including multispectral bands, normalised difference vegetation index, elevation, slope, curvature, topographic position index, and topographic wetness index. A High-Resolution Boundary U-Net (HRBoundaryUNet) model was implemented and compared with selected benchmark segmentation models using mean intersection over union (mIoU) and macro F1-score. To reduce spatial autocorrelation and patch-overlap leakage, model evaluation was performed using a spatially buffered stratified validation scheme, with performance further assessed using per-class intersection over union (IoU) and F1-score. Spatial discrepancy analysis was also applied to distinguish direct agreement, boundary generalisation, and spatial misclassification between the CNN-enhanced map and the reference map. Field investigations from unmanned aerial vehicle surveys and soil coring were used as point-based supporting evidence to corroborate geomorphological interpretation. The results demonstrate that CNN-based segmentation can serve as a reproducible and semi-automated mapping support tool for regional geomorphological mapping. The proposed workflow complements expert-based mapping by providing quantitative spatial evaluation, improving consistency in map generation, and offering a transparent basis for subsequent expert interpretation.