Extracting water information from remote-sensing images is of great research significance for applications such as water resource protection and flood monitoring. Current water extraction methods aggregated richer multi-level features to enhance the output results. In fact, there is a difference in the requirements for the water body and the water boundary. Indiscriminate multi-feature fusion can lead to perturbation and competition of information between these two types of features during the optimization. Consequently, models cannot accurately locate the internal vacancies within the water body with the external boundary. Therefore, this paper proposes a water feature extraction network with spatial partitioning and feature decoupling. To ensure that the water features are extracted with deep semantic features and stable spatial information before decoupling, we first design a chunked multi-scale feature aggregation module (CMFAM) to construct a context path for obtaining deep semantic information. Then, an information interaction module (IIM) is designed to exchange information between two spatial paths with two fixed resolution intervals and the two paths through. During decoding, a feature decoupling module (FDM) is developed to utilize internal flow prediction to acquire the main body features, and erasing techniques are employed to obtain boundary features. Therefore, the deep features of the water body and the detailed boundary information are supplemented, strengthening the decoupled body and boundary features. Furthermore, the integrated expansion recoupling module (IERM) module is designed for the recoupling stage. The IERM expands the water body and boundary features using expansion and adaptively compensates the transition region between the water body and boundary through information guidance. Finally, multi-level constraints are combined to realize the supervision of the decoupled features. Thus, the water body and boundaries can be extracted more accurately. A comparative validation analysis is conducted on the public datasets, including the gaofen image dataset (GID) and the gaofen2020 challenge dataset (GF2020). By comparing with seven SOTAs, the results show that the proposed method achieves the best results, with IOUs of 91.22 and 78.93, especially in the localization of water bodies and boundaries. By applying the proposed method in different scenarios, the results show the stable capability of the proposed method for extracting water with various shapes and areas.

Building extraction from high-resolution remote sensing imagery remains challenging due to spectral heterogeneity, complex background interference, and incomplete boundary delineation. Thus, we propose GWNet, which integrates gated Mamba-CNN modeling with wavelet-based boundary enhancement. Specifically, a Gated Mamba-CNN Module (GMC) is embedded into the medium- and low-resolution branches to jointly capture local texture features and long-range dependencies. In addition, a channel-wise gating mechanism is introduced to adaptively balance global contextual information and local structural details, thereby alleviating fragmented predictions and internal holes within the same building caused by variations in roof materials, while reducing the misclassification between buildings and background objects such as roads and bare land. Furthermore, a Wavelet Boundary Optimization Module (WBO) is designed to exploit multi-directional high-frequency components extracted by fixed Haar wavelet filters, thereby enhancing the representation of building boundaries and corners. This design effectively mitigates boundary blurring, incomplete contours, and missed detections caused by the loss of high-frequency edge information during downsampling. Extensive experiments on four public datasets, namely WHU, Massachusetts, WHU Satellite I, and Potsdam, demonstrate the effectiveness and robustness of GWNet across diverse spatial resolutions and scene complexities. Specifically, GWNet achieves IoU/BIoU scores of 90.68%/66.88% on the WHU dataset, 73.02%/93.19% on the Massachusetts dataset, 63.86%/83.77% on the WHU Satellite I dataset, and 83.21%/58.96% on the Potsdam dataset, consistently outperforming several competitive methods. Qualitative results further confirm that GWNet produces more complete building regions and sharper, more continuous boundaries. These findings validate the effectiveness of the proposed global–local feature extraction mechanism and wavelet-based boundary enhancement strategy.

With the increasing diversity of remote sensing image acquisition conditions and imaging scenarios, building extraction models often experience significant performance degradation in cross-dataset applications due to variations in sensors and scene characteristics. Improving their cross-domain generalization ability has therefore become a critical research problem. To address the challenges of appearance style discrepancy and feature distribution shift in cross-domain building extraction, this paper proposes WCMNet, a wavelet-guided and CNN–Mamba hybrid network for unsupervised domain adaptation in building extraction. Specifically, a Mamba Wavelet Alignment (MWA) module is designed to align low-frequency style information in the wavelet domain while preserving directional high-frequency edge structures, thereby mitigating cross-domain appearance discrepancies and reducing structural degradation during domain translation. In addition, a Global–Local Mamba Block (GLMB) is developed to jointly model local textures and global semantic dependencies. In GLMB, the CNN branch captures fine-grained local details and boundary cues, while the Mamba branch models long-range contextual information; an adaptive gated fusion mechanism further integrates the two types of features. Experimental results on six cross-domain transfer tasks across the WHU, Massachusetts, and Potsdam datasets demonstrate that WCMNet consistently outperforms existing state-of-the-art domain adaptation methods. In particular, WCMNet achieves an average IoU of 65.13% and an average BIoU of 74.80% across all transfer settings, with improvements of up to 27.35 percentage points in IoU and 38.32 percentage points in BIoU compared with the strongest competing methods. These results demonstrate that the proposed MWA and GLMB effectively improve building completeness, boundary delineation accuracy, and cross-domain robustness.