Monitoring Anthropogenically Disturbed Parcels with Soil Erosion Dynamics Change Based on an Improved SegFormer

Abstract

Amidst burgeoning socioeconomic development, anthropogenic activities have exacerbated soil erosion. This erosion, characterized by its brief duration, high frequency, and considerable environmental degradation, presents a major challenge to ecological systems. Therefore, it is imperative to regulate and remediate erosion–prone, anthropogenically disturbed parcels, with dynamic change detection (CD) playing a crucial role in enhancing management efficiency. Currently, traditional methods for change detection, such as field surveys and visual interpretation, suffer from time inefficiencies, complexity, and high resource consumption. Meanwhile, despite advancements in remote sensing technology that have improved the temporal and spatial resolution of images, the complexity and heterogeneity of terrestrial cover types continue to limit large–scale dynamic monitoring of anthropogenically disturbed soil erosion parcels (ADPSE) using remote sensing techniques. To address this, we propose a novel ISegFormer model, which integrates the SegFormer network with a pseudo–residual multilayer perceptron (PR–MLP), cross–scale boundary constraint module (CSBC), and multiscale feature fusion module (MSFF). The PR–MLP module improves feature extraction by capturing spatial contextual information, while the CSBC module enhances boundary prediction through high– and low–level semantic guidance. The MSFF module fuses multiscale features with attention mechanisms, boosting segmentation precision for diverse change types. Model performance is evaluated using metrics, such as precision, recall, F1–score, intersection over union (IOU), and mean intersection over union (mIOU). The results demonstrate that our improved model performs exceptionally well in dynamic monitoring tasks for ADPSE. Compared to five other models, our model achieved an mIOU of 72.34% and a Macro–F1 score of 83.55% across twelve types of ADPSE changes, surpassing the other models by 1.52–2.48% in mIOU and 2.25–3.64% in Macro–F1 score. This work provides a theoretical and methodological foundation for policy–making in soil and water conservation departments.

Keywords

• change detection
• anthropogenic activities
• dynamic monitoring
• segformer
• remote sensing
• deep learning