A Linear Attention Framework with Dual-Axis Multi-Scale Fusion for Fine-Grained Eucalyptus Change Detection

Abstract

The fine-scale monitoring of plantation cover disappearance and appearance is challenging because these changes are often expressed as weak within-class variations in high-resolution images. This study proposes MLLAForestCD, a three-class pixel-level semantic change-detection network for Eucalyptus plantations. The model uses an MLLA encoder to model the long-range spatial context with efficient linear attention, while a dual-axis change extractor reorganizes paired bi-temporal features through complementary layouts before contextual interaction. Multi-scale fusion then combines semantic cues with boundary-level details. We further construct the Eucalyptus Change Detection Dataset (ECDD), which contains plantation scenes with weak spectral contrast, fragmented boundaries, and directional canopy textures. Under the retained patch-level training/validation split, MLLAForestCD achieves an F1-score of 96.66% and an mIoU of 93.59%. After separate training and evaluation based on WHU-CD, it achieves an F1-score of 97.29% and an IoU of 90.10%; this result reflects performance under an independent WHU-CD training protocol. Finally, annual change maps from 2020 to 2023 are used to derive the most recently detected plantation-appearance time within the observation window. The resulting product is interpreted as a recent stand-renewal event map and requires independent forestry records before biological stand age can be inferred.