DWFF-Net : A Multi-Scale Farmland System Habitat Identification Method with Adaptive Dynamic Weight

TL;DR

This paper introduces DWFF-Net, a novel multi-scale habitat identification model using adaptive dynamic weight fusion, achieving high accuracy in fine-grained cultivated land habitat mapping with sub-meter precision.

Contribution

It develops a new habitat dataset and proposes DWFF-Net with adaptive feature fusion and a hybrid loss, improving segmentation accuracy over existing models.

Findings

Achieved 69.79% mIoU and 80.49% F1-score on the dataset.

Outperformed baseline networks by 2.1% in mIoU.

Effectively improves micro-habitat segmentation, such as field ridges.

Abstract

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)