Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation

BASeg: Boundary-Aware Remote Sensing Segmentation with Structural Penalties

A Mahalanobis-Angle Boundary Loss (MABL) is proposed that explicitly enhances boundary and shape consistency and is introduced, built upon MABL, a boundary- aware remote sensing segmentation framework with Struc- tural Penalties.

Rcl-sam: a parameter-efficient segment anything model for high-resolution remote sensing cultivated land extraction

AB-SAM: A SAM-Based Asymmetric Boundary-Aware Model for the Semantic Segmentation of Small and Medium-Sized Landslides

The results demonstrate that AB-SAM provides a practical parameter-efficient framework for automated, hint-free landslide segmentation, although further evaluation across additional regions, sensors, and landslide-size distributions remains necessary.

Zero-shot multi-class semantic segmentation of remote sensing images using SAM 2 with prior database information

This work presents a new method for using the VFM Segment Anything Model 2 (SAM 2) for multi-class semantic segmentation of Sentinel-2 images that does not require training data and achieves an overall accuracy of up to 93% at pixel-level using polygon mask prompts.

MsRE: Toward Efficient Remote Sensing Segmentation via Vision Foundation Models

Vision foundation models (VFMs) pretrained on large-scale datasets have significantly improved performance in remote sensing semantic segmentation. However, existing methods typically rely on full fine-tuning, which requires updating all model parameters. Instead of updating the full parameter set, parameter-efficient fine-tuning (PEFT) achieves competitive performance by optimizing only a small subset of parameters. Despite its success, most existing PEFT methods are mainly designed for natural image tasks and fail to account for the unique multiscale characteristics of remote sensing images. To address these challenges, we propose multi-scale cognitive feature refinement (MsRE) tuning, a novel PEFT method tailored for remote sensing semantic segmentation. In particular, MsRE captures multiscale contextual information by applying cognitive operations with different cognitive fields to intermediate features of the backbone. It then introduces a set of learnable tokens to establish interactions with features at different scales, enabling precise feature refinement and progressive feature propagation across network layers. This mechanism enhances the model’s ability to understand complex remote sensing scenes and improves downstream segmentation performance. With significantly fewer trainable parameters, MsRE provides an efficient yet effective solution for adapting VFMs to remote sensing segmentation tasks. Extensive experiments demonstrate that MsRE achieves competitive segmentation performance with substantially fewer trainable backbone parameters, providing a favorable balance between accuracy and parameter efficiency. The project is available at http://woldier.top/MsRE

RSUS: A Novel Upsampling Layer for Semantic Segmentation Network of Remote Sensing Images

Semantic segmentation of remote sensing images (RSIs) often struggles with boundary blurring, structural discontinuity, and category confusion due to limitations in conventional interpolation and dynamic upsampling methods. This article proposes RSUS, a new upsampling layer designed to preserve semantic consistency and spatial structure. RSUS consists of three components: 1) global context vector aggregation (GCVA) for content-aware prediction kernels that introduce global priors into local feature reconstruction; 2) cross-scale anisotropic implicit positional encoding (CAIPE) for direction-sensitive spatial deformation and structural alignment; and 3) adaptive high-frequency structure gating (AHSG) to enhance boundary-related frequency responses. In addition, persistent homology (PH) is used as a topological analysis tool to assess connectivity and structure preservation beyond pixel-level metrics. Extensive experiments on five datasets (International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen, ISPRS Potsdam, LoveDA, UAVid, and Xining) demonstrate that RSUS improves performance across mainstream segmentation networks, outperforming advanced upsampling methods. Analyses show RSUS alleviates structural misalignment, improves category consistency, and recovers fine-grained boundaries in remote sensing segmentation. The source code is available at: https://github.com/Ronin-711/RSUS