准确识别黄土地貌对于地貌制图、地形演化分析及地表过程研究至关重要。然而，现有方法在统一框架内联合表征地貌语义与空间结构特征方面仍存在局限。此外，数字高程模型（DEM）数据中的黄土地貌边界通常呈现渐变过渡特征，进一步增加了地貌识别与空间范围表征的难度。为解决上述问题，本研究提出LoessFormer——一种面向黄土地貌识别的自监督框架，支持语义与边界统一建模。具体而言，设计了两项互补的自监督任务：掩码块预测（Masked Patch Prediction, MPP）与块序预测（Patch Order Prediction, POP），以从DEM数据中学习局部连续性与全局空间关系。这两项任务被整合至基于Transformer的架构中，从而增强对复杂地貌结构的表征能力。在下游微调阶段，采用面向对象的统一建模策略，同步学习地貌语义与空间范围表征。在中国黄土高原典型区域开展的实验表明，LoessFormer在多个评估指标（包括[email protected]、F1分数和IoU）上均优于现有主流方法。其中，所提方法达到55.68%的[email protected]，验证了其在复杂黄土地貌识别及其空间结构表征方面的有效性。

Accurate recognition of loess landforms is essential for geomorphological mapping, terrain evolution analysis, and surface process studies. However, existing methods remain limited in jointly representing geomorphological semantics and spatial structural characteristics within a unified framework. In addition, loess landform boundaries in DEM data usually exhibit gradual transitions, which further increases the difficulty of landform recognition and spatial extent representation. To address these issues, this study proposes LoessFormer, a self-supervised framework for loess landform recognition with unified semantic and boundary modelling. Specifically, two complementary self-supervised tasks, namely Masked Patch Prediction (MPP) and Patch Order Prediction (POP), are designed to learn local continuity and global spatial relationships from DEM data. These tasks are integrated into a Transformer-based architecture to enhance the representation capacity for complex geomorphological structures. During downstream fine-tuning, an object-level unified modelling strategy is adopted to jointly learn landform semantics and spatial extent representations. Experiments conducted in typical regions of the Chinese Loess Plateau demonstrate that LoessFormer achieves superior performance over existing mainstream methods across multiple evaluation metrics, including [email protected], F1-score, and IoU. In particular, the proposed method achieves an [email protected] of 55.68%, demonstrating its effectiveness in recognising complex loess landforms and representing their spatial structures.