Semantic segmentation of high-resolution remote sensing images is vital in downstream applications such as land-cover mapping, urban planning, and disaster assessment. Existing Transformer-based methods suffer from the constraint between accuracy and efficiency, while the recently proposed Mamba is renowned for being efficient. Therefore, to overcome the dilemma, we propose UNetMamba, a UNet-like semantic segmentation model based on Mamba. It incorporates a Mamba segmentation decoder (MSD) that can efficiently decode the complex information within high-resolution images, and a local supervision module (LSM), which is train-only but can significantly enhance the perception of local contents. Extensive experiments demonstrate that UNetMamba outperforms the state-of-the-art (SOTA) methods with mIoU increased by 0.87% on LoveDA and 0.39% on ISPRS Vaihingen while achieving high efficiency through the lightweight design, less memory footprint, and reduced computational cost. The source code is available at https://github.com/EnzeZhu2001/UNetMamba .

Disparity estimation from high-resolution satellite stereo images is critical for 3D reconstruction but remains challenging due to large disparities, complex structures, and textureless regions. To address this, we propose a Semantic-Aware Optical Flow (SAOF) framework for fine-grained disparity estimation, which enhances optical flow-based via a multi-level optimization incorporating sub-top pyramid re-PatchMatch, scale-adaptive matching windows, and multi-feature cost refinement. For improving the spatial consistency of the resulting disparity map, SAMgeo-Reg is utilized to produce semantic prototypes, which are used to build guidance embeddings for integration into the optical flow estimation process. Experiments on the US3D dataset demonstrate that SAOF outperforms state-of-the-art methods across challenging scenarios. It achieves an average endpoint error (EPE) of 1.317 and a D1 error of 9.09%.