# Multi-Scale Feature Learning for Farmland Segmentation Under Complex Spatial Structures

## TL;DR

This paper introduces CSMNet, a new model for accurately segmenting farmland in complex satellite images, achieving high performance compared to existing methods.

## Contribution

The novel CSMNet model improves farmland segmentation by integrating multi-scale fusion, adaptive attention, and a hybrid loss function.

## Key findings

- CSMNet achieves an IoU of 90.85%, outperforming existing models like Unet++ by 8.92%.
- The model's adaptive attention module enhances boundary delineation in complex agricultural regions.
- Hybrid loss function improves robustness for small and fragmented farmland parcels.

## Abstract

Fragmented, irregular, and scale-heterogeneous farmland parcels introduce high spatial complexity into high-resolution remote sensing imagery, leading to boundary ambiguity and inter-class spectral confusion that hinder effective feature discrimination in semantic segmentation. To address these challenges, we propose CSMNet, which adopts a ConvNeXt V2 encoder for hierarchical representation learning and a multi-scale fusion architecture with redesigned skip connections and lateral outputs to reduce semantic gaps and preserve cross-scale information. An adaptive multi-head attention module dynamically integrates channel-wise, spatial, and global contextual cues through a lightweight gating mechanism, enhancing boundary awareness in structurally complex regions. To further improve robustness, a hybrid loss combining Binary Cross-Entropy and Dice loss is employed to alleviate class imbalance and ensure reliable extraction of small and fragmented parcels. Experimental results from Nong’an County demonstrate that the proposed model achieves superior performance compared with several state-of-the-art segmentation methods, attaining a Precision of 95.91%, a Recall of 93.95%, an F1-score of 94.92%, and an IoU of 90.85%. The IoU exceeds that of Unet++ by 8.92% and surpasses PSPNet, SegNet, DeepLabv3+, TransUNet, SeaFormer and SegMAN by more than 15%, 10%, 7%, 6%, 5% and 2%, respectively. These results indicate that CSMNet effectively improves information utilization and boundary delineation in complex agricultural landscapes.