Segmentation of mining-disturbed land is important for eco-geological environment monitoring. Although existing methods possess strong segmentation capabilities, mining-disturbed land exhibits irregular edges, different spatial sizes, and global texture variability, which lead to difficulties in extracting discriminative features, thereby limiting segmentation accuracy. This study built an RGB-based binary semantic segmentation dataset, covering typical mining-disturbed land in Fujian Province of China. Then a spatial prompt and wavelet Mamba-based multi-scale cross-domain feature fusion network (SWDF-Net) was proposed. (1) Wavelet Mamba-based dual-frequency collaborative enhancement module: high-low frequency information was decoupled by wavelet transform, and collaboratively enhanced by fusion of multiple local details and Mamba-guided global information. It can highlight the high-frequency edge features and low-frequency texture patterns of mining-disturbed land. (2) Cross-domain feature alignment and fusion module: cross-domain statistical calibration and channel conditional modulation were used to narrow spatial–frequency feature distribution gap, which facilitates cross-domain feature alignment and fusion. (3) Spatial prompt-based multi-scale feature weighted fusion module: pixel-level weight maps were generated by spatial prior prompt derived from a spatial decoder and edge-gated branch, which adaptively fuses former multi-scale dual-domain features. The SWDF-Net achieved the best Intersection over Union of 72.22% for mining-disturbed land and performed competitively on the ISPRS Vaihingen and Potsdam datasets.

Segmentation of mining-disturbed land is of great significance for eco-geological environment monitoring. Although existing methods possess strong segmentation capabilities, mining-land exhibits irregular edges, different spatial size, and global texture variability, which lead to difficulties in extracting discriminative features, thereby limiting the accuracy performance. This study first built an RGB-based binary semantic segmentation dataset, covering typical mining-lands in Fujian Province of China. Then a spatial prompt and wavelet Mamba-based multi-scale cross-domain feature fusion network (SWDF-Net) was proposed. (1) Wavelet Mamba-based dual-frequency collaborative enhancement module: high-low frequency information was decoupled by wavelet transform, and further collaboratively enhanced by fusion of multiple local details and Mamba-guided global information. It can highlight mining-lands' high-frequency edge features and low-frequency texture patterns. (2) Cross-domain feature alignment and fusion module: cross-domain statistical calibration and channel conditional modulation were used to narrow spatial-frequency feature distribution gap, which facilitates cross-domain feature alignment and effective fusion. (3) Spatial prompt-based multi-scale feature weighted fusion module: pixel-level weight maps were generated by spatial prior prompt that derived from a spatial decoder and edge gate-based branch, which adaptively fuses former multi-scale dual-domain features. The SWDF-Net achieved best Intersection over Union of 72.22% for mining-land and performed competitively on the ISPRS Vaihingen dataset.

High-resolution remote sensing image semantic segmentation (HRSS) is a fundamental yet critical task in the field of Earth observation. However, it has long faced the challenges of high inter-class similarity and large intra-class variability. Existing approaches often struggle to effectively inject abstract yet strongly discriminative semantic knowledge into pixel-level feature learning, leading to blurred boundaries and class confusion in complex scenes. To address these challenges, we propose Bidirectional Co-Refinement Framework for HRSS (BiCoR-Seg). Specifically, we design a Heatmap-driven Bidirectional Information Synergy Module (HBIS), which establishes a bidirectional information flow between feature maps and class embeddings by generating class-level heatmaps. Based on HBIS, we further introduce a hierarchical supervision strategy, where the interpretable heatmaps generated by each HBIS module are directly utilized as low-resolution segmentation predictions for supervision, thereby enhancing the discriminative capacity of shallow features. In addition, to further improve the discriminability of the embedding representations, we propose a cross-layer class embedding Fisher Discriminative Loss to enforce intra-class compactness and enlarge inter-class separability. Extensive experiments on the LoveDA, Vaihingen, and Potsdam datasets demonstrate that BiCoR-Seg achieves outstanding segmentation performance while offering stronger interpretability. The released code is available at https://github.com/ShiJinghao566/BiCoR-Seg.