This study introduced a novel two-step treatment to enhance the waterproofing, dimensional stability, and self-cleaning capabilities of ancient architectural wood. The process was initiated with the immersion of wood in an organic hybrid sol, composed of an acidic methyltrimethoxysilane (MTMS)-based silica sol and polyvinyl alcohol (PVA), which effectively sealed the wood’s inherent pores and cracks to mitigate degradation effects caused by aging, fungi, and insects. Subsequently, the treated wood surface was modified with an alkaline MTMS-based silica sol to form a functional superhydrophobic protective layer. The modification effectiveness was meticulously analyzed using advanced characterization techniques, including scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), Fourier-transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD). The results demonstrated substantial improvements: the modified wood’s water contact angle (WCA) reached 156.0°, and the sliding angle (SA) was 6.0°. Additionally, the modified wood showed a notable reduction in water uptake and moisture absorption, enhancing its dimensional stability. The superhydrophobic surface endowed the wood with excellent self-cleaning properties and robust resistance to pollution. Enhanced mechanical durability of superhydrophobic surface was observed under rigorous testing conditions, including sandpaper abrasion and tape peeling. Furthermore, the modification improved the thermal stability, compressive strength, and storage modulus of the wood. Collectively, these enhancements render this modification a potent methodology for the preservation and functional augmentation of historic architectural woodwork.

For atypical traditional villages, their invaluable historical traces and cultural memories are preserved in the existing village landscapes. Rapid and accurate acquisition of the spatial information of various surface elements in a village is an important prerequisite for a scientific, reasonable, feasible planning and design scheme for conserving, progressing, and developing atypical villages. Taking Qianfeng Village as an example, this research proposes the atypical traditional village landscape classification model based on unmanned aerial vehicle (UAV) imagery (ATVLUI) by virtue of the UAV RGB images and the object-oriented fuzzy logic membership classification technique that extracts objects according to their spectrums, textures, geometries, and context relationships, aiming at precise extraction of atypical traditional village landscapes. Based on the landscape information, the landscape pattern indexes are calculated to explore the spatial distribution characteristics of different landscapes and analyze the current conditions of Qianfeng Village as the epitome of atypical traditional villages. Accordingly, suggestions for protecting, planning, and developing atypical villages are proposed. The results show that: (1) the ATVLUI boasts excellent identification for village landscapes in a complex scenario, with a classification accuracy for traditional structures of 84%, an overall accuracy of 93%, and a Kappa coefficient of 0.89. This model is proven superior to K-nearest neighbors (KNN), decision tree (DT), and random tree (RT); (2) according to the area and proportion calculations, the structures account for 33.94% of Qianfeng Village’s total area, in which 29.69% and 4.25% are modern and traditional structures, respectively. The number of traditional structures is 202, accounting for 13% of the total number of structures; (3) within the village, connectivity between and extension of the modern structures can be recognized, suggesting a trajectory where the traditional structures are being gradually substituted by modern ones. The ecological environment at the periphery of the village is favorable. The building-to-building common boundaries are long. The modern structures are densely distributed. The discretely distributed traditional structures gather as small clusters. In general, different structures are highly interlaced to form a fragmented distribution pattern.