S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data

Accurately extracting forest land and understanding its spatial distribution are crucial for forest monitoring and management. However, variations in tree species, human activities, and natural disturbances create diverse and distinct forest land characteristics in remote sensing images, posing challenges for precise forest land extraction. To address these challenges, we propose a spatial-semantic information fusion network (S2-IFNet) integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data. S2-IFNet employs a dual-branch network to separately extract the spatial and semantic features of forest land. In the semantic branch, two modules are introduced: a boundary enhancement module using the Sobel operator to capture forest land boundary details, and an attention module to strengthen the feature representation capability. Finally, a spatial-semantic fusion module effectively combines the spatial, semantic, and boundary detail information to improve the forest land extraction accuracy. S2-IFNet was evaluated across five regions in different global climate zones, with a comparative analysis conducted against four forest land-cover products in Yunnan, China. The results show that S2-IFNet can achieve an overall accuracy exceeding 90%, demonstrating its strong forest land extraction capability. Compared to the different forest land extraction models, S2-IFNet shows a superior performance, with ablation experiments confirming the effectiveness of each module. In particular, the boundary feature enhanced spatial-semantic fusion strategy enables S2-IFNet to focus more precisely on the boundaries and range of forest land, thereby enhancing the extraction accuracy. Meanwhile, S2-IFNet can adapt to complex scenarios, including varying forest density and similar object confusion. Furthermore, S2-IFNet can achieve results that are superior to the four other products.