DAM-CGNet: Semantic segmentation-based approach for valley-bottom extraction from digital elevation models

The accurate extraction of valley bottoms from digital elevation models (DEMs) is crucial for hydrological and geomorphological analyses of mountainous landscapes. However, threshold settings rely on manual intervention; roads near valley bottoms resemble valley-bottom features, and thresholds cannot effectively adapt to valleys of various shapes, leading to low extraction accuracy in existing methods, particularly in narrow V-shaped valleys. To address these issues, this study developed a semantic segmentation approach called a Dense-based Attention Merging Context Guided Network (DAM-CGNet). Without relying on thresholds, this method effectively excludes roads on hillslopes and enhances the recognition of steep feature changes at valley boundaries, enabling the extraction of valley bottoms of various shapes. Key improvements include: (1) incorporating the convolutional block attention module (CBAM) to enhance feature reuse in the information flow, employing attention mechanisms to suppress irrelevant feature responses and focus on valley boundary features; (2) using the dense connection strategy of DenseNet to rebuild the feature flow, helping the model keep important valley-bottom details in deep layers and better recognize small and narrow valleys; and (3) addressing the limitations of single-channel DEM representation by evaluating various input combinations, ultimately selecting DEM, topographic position index (TPI), and slope as effective inputs for valley-bottom extraction. Experiments using semantic segmentation models and conventional methods validated the effectiveness of the proposed method. Specifically, DAM-CGNet achieved high accuracy on the test set (MPA: 90.15 %, MIoU: 84.18 %, FWIoU: 92.99 %) and outperformed conventional methods in extracting valley bottoms of various shapes. This method, without a manual threshold setting as in conventional approaches, enhances valley bottom extraction precision and provides a new perspective for subsequent valley bottom width calculations.