Addressing the current lack of a standardized habitat classification system for cultivated land ecosystems, incomplete coverage of the habitat types, and the inability of existing models to effectively integrate semantic and texture features-resulting in insufficient segmentation accuracy and blurred boundaries for multi-scale habitats (e.g., large-scale field plots and micro-habitats)-this study developed a comprehensively annotated ultra-high-resolution remote sensing image dataset encompassing 15 categories of cultivated land system habitats. Furthermore, we propose a Dynamic-Weighted Feature Fusion Network (DWFF-Net). The encoder of this model utilizes a frozen-parameter DINOv3 to extract foundational features. By analyzing the relationships between different category images and feature maps, we introduce a data-level adaptive dynamic weighting strategy for feature fusion. The decoder incorporates a dynamic weight computation network to achieve thorough integration of multi-layer features, and a hybrid loss function is adopted to optimize model training. Experimental results on the constructed dataset demonstrate that the proposed model achieves a mean Intersection over Union (mIoU) of 69.79% and an F1-score of 80.49%, outperforming the baseline network by 2.1% and 1.61%, respectively. Ablation studies further confirm the complementary nature of multi-layer feature fusion, which effectively improves the IoU for micro-habitat categories such as field ridges. This study establishes a habitat identification framework for cultivated land systems based on adaptive multi-layer feature fusion, enabling sub-meter precision habitat mapping at a low cost and providing robust technical support for fine-grained habitat monitoring in cultivated landscapes. (The complete code repository can be accessed via GitHub at the following URL: https://github.com/sysau/DWFF-Net)

翻译：针对当前耕地生态系统缺乏标准化的生境分类体系、生境类型覆盖不完整，以及现有模型难以有效融合语义与纹理特征，导致多尺度生境（如大尺度田块与微生境）分割精度不足、边界模糊的问题，本研究构建了一个包含15类耕地系统生境的、经过全面标注的超高分辨率遥感影像数据集。进一步，我们提出了一种动态加权特征融合网络（DWFF-Net）。该模型的编码器采用参数冻结的DINOv3提取基础特征。通过分析不同类别图像与特征图之间的关系，我们引入了一种数据层面的自适应动态加权策略进行特征融合。解码器集成了一个动态权重计算网络，以实现多层特征的深度融合，并采用混合损失函数优化模型训练。在构建的数据集上的实验结果表明，所提模型的平均交并比（mIoU）达到69.79%，F1分数为80.49%，分别比基线网络高出2.1%和1.61%。消融研究进一步证实了多层特征融合的互补性，有效提升了田埂等微生境类别的IoU。本研究建立了一个基于自适应多层特征融合的耕地系统生境识别框架，能够以低成本实现亚米级精度的生境制图，为耕地景观的精细化生境监测提供了有力的技术支持。（完整代码仓库可通过GitHub访问，URL：https://github.com/sysau/DWFF-Net）