High-resolution image segmentation for landscape applications has garnered significant attention, particularly in the context of ultra-high-resolution (UHR) imagery. Current segmentation methodologies partition UHR images into standard patches for multiscale local segmentation and hierarchical reasoning. This creates a pressing dilemma, where the trade-off between memory efficiency and segmentation quality becomes increasingly evident. This paper introduces the Multilevel Contexts Weighted Coupling Transformer (WCTNet) for UHR segmentation. This framework comprises the Mult-level Feature Weighting (MFW) module and Token-based Transformer (TT) designed to weigh and couple multilevel semantic contexts. First, we analyze the multilevel semantics within a local patch without image-level contextual reasoning. It avoids complex image-level contextual associations and eliminates the misleading information carried. Second, MFW is developed to weigh shallow and deep features for enhancing object-related attention at different grain sizes from multilevel semantics. Third, the TT module is introduced to couple multilevel semantic contexts and transform them into semantic tokens using spatial attention. Then, we can capture token interactions and obtain clearer local representations. The suggested contextual weighting and coupling of single-scale patches empower WCTNet to maintain a well-balanced relationship between accuracy and computational overhead. Experimental results show that WCTNet achieves state-of-the-art performance on two UHR datasets of DeepGlobe and Inria Aerial.

This study describes the application of aluminum sulfate Al₂(SO₄)₃, boric acid H₃BO₃, phosphoric acid H₃PO₄ (Al–B–P) and amphiprotic surfactant material synthesis by the sol-gel process, which were adopted as novel precursors for wood modification. The efficacy of Al–B–P-treated wood was tested against Poria placenta and Coriolus versicolor. Untreated wood samples had higher mass losses (>40%) compared to the treated sample, which had the lowest wood mass losses (of 4%) against P. placenta and C. versicolor. To analyze the reaction mechanism of Al–B–P wood, the mechanical properties, chemical structure, crystallinity, thermal analysis, binding energy and wettability was examined by modulus of rupture (MOR), modulus of elasticity (MOE), Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), Thermogravimetric analysis (TG) and X-ray photoelectron spectroscopy (XPS), respectively. Scanning electron microscopy- energy-dispersive X-ray spectroscopy (SEM-EDS) confirmed the wood colonization by fungi, and was used to identify the microstructures and morphologies changes that occurred in the cells during degradation by white and brown-rot fungi. At the same time, X-ray photoelectron spectroscopy (XPS) was employed to analyze the physical and chemical properties of the samples. Therefore, the study confirmed that Al–B–P and amphiprotic surfactant could replace the traditional wood preservative products, and have the potential to extend the service life of wood, particularly in soil contact and outdoor usage.