## 1 Introduction
However, the presence of noisy pixels in a landslide image usually has a high local gradient, which can lead to erroneous detection of landslide edges [22]. Region-growing methods expand the region from selected seed points, and the segmentation effect can be flexibly adjusted by modifying the growth rule or the similarity threshold. However, as the image resolution and the size of a landslide region increase, the growth process requires the analysis of a greater number of pixels, which reduces computational efficiency [23; 24]. In order to overcome the limitations inherent to traditional image processing techniques, machine learning-based algorithms have been progressively implemented in the context of landslide image detection. In the initial stages of research, traditional machine learning algorithms were employed. Among these, support vector machines (SVMs) reduce the sensitivity to noisy data by maximizing the bounding interval [25], and random forests reduce the overfitting of a single decision tree to noise by integrating multiple decision trees [26] and also utilize manually extracted features such as the texture, shape, and spectral features for classification [27; 28]. However, they all rely heavily on feature engineering, which limits their ability to capture complex, high-dimensional feature distributions and their effectiveness in multi-scale feature scenarios. In recent years, the rapid development of deep learning technology has demonstrated considerable potential in the detection of landslide images, establishing itself as a pivotal solution to the challenge of landslide image segmentation [29]. Convolutional neural networks (CNNs) are capable of unparalleled feature extraction and pattern recognition, and they are adept at dealing with complex backgrounds and multi-scale feature distributions [30]. Architectures such as U-Net [31] and U-Net++ [32] have demonstrated considerable success in medical [33] and remote sensing image segmentation [34] and also shown great potential in landslide detection [35; 36]. Nevertheless, the identification of minor landslide formations remains a significant challenge due to their irregular morphologies and the paucity of available marker data. To address these limitations, researchers have proposed the integration of multi-scale feature fusion and context-aware network architectures [37]. Models such as PSPNet [38] and DeepLab v3+ [39] have further enhanced segmentation accuracy through the incorporation of attention mechanisms [40] and multi-scale learning strategies [41]. Additionally, migration learning has been employed to mitigate the constraints of small datasets by leveraging pretrained models, thereby markedly improving generalization performance [42; 43]. Despite these advances, several critical challenges persist. Current models often under-perform when segmenting small, fragmented landslide regions with ambiguous or complex boundaries. Furthermore, the limited scale of available landslide datasets makes deep learning models susceptible to overfitting, compromising generalizability. To address these challenges, the introduction of Kolmogorov-Arnold networks (KANs) offers a promising avenue for landslide segmentation, leveraging their capacity for modeling complex nonlinear features [44; 45]. Concurrently, deep learning models incorporating multi-scale attention mechanisms [46] and those leveraging transfer learning techniques [47] have demonstrated outstanding performance in remote sensing image processing. To tackle the challenges in landslide image segmentation, this study introduces a novel model, the Large Kernel Nested UKAN (LKN-UKAN), which is built upon an enhanced U-Net++ architecture and further refined with transfer learning techniques. The model incorporates several key innovations to boost segmentation accuracy, boundary clarity, and adaptability, particularly in data-scarce environments.