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China is the main technology research and development country in the world’s bamboo industry, as well as the main producer and exporter of bamboo products. It ranks at the international leading level in terms of bamboo forest area, stock volume, bamboo product output and scientific research level. However, through literature review, it is found that the development of bamboo products in the modern social environment still faces many challenges. The author searched the keyword “bamboo product design” through CNKI and SCOPUS database systems, and initially retrieved 562 literatures. According to the screening criteria “Bamboo Product Design Status and Strategies”, and excluding redundant and repetitive studies, 76 papers were finally screened and identified. The author uses the A-type graphical tool of the KJ method to combine, classify and analyze the keywords in the “problem” literature, and use the relationship between these materials to organically combine and summarize these facts. It is found that there is a gap in capability between bamboo product designers and craftsmen. What is the gap between them? How to narrow the gap between them and improve their design capability? This is the research question of this study. To a certain extent, it is one of the key factors for the insufficient design innovation of bamboo products and the inability to meet consumer demands. Therefore, this study constructs a strategic framework of five capability dimensions by reviewing the literature on design capability management, combining product design standards and design capability demand theory, and incorporating the unique relevant factors of bamboo product design. The framework aims to improve the knowledge system and professional capability of craftsmen and designers to enhance the diversification and innovation of bamboo products, help the bamboo industry to clarify the market positioning of products, and meet people’s needs for bamboo products in modern life. [ABSTRACT FROM AUTHOR]

Efforts to evaluate the susceptibility of debris flows in large areas, especially in mountainous regions, are often hampered by the alpine and canyon terrain. This paper proposes a convolution neural network (CNN) model named dense residual shuffle net (DRSNet). It is successfully applied to Nujiang Prefecture in Yunnan Province of China, a typical alpine area with frequent debris flows. DRSNet uses digital elevation model, remote sensing, lithology, soil type and precipitation data as input. First, dense connection and residual structure were used to extract the shallow features of various data. Next, channel shuffle, fuse block and fully connection were applied to strengthen the correlation between different shallow features and give inner danger scores. Finally, precipitation as the activation factor was introduced giving the valleys susceptibility. To verify the feasibility of DRSNet, comparative tests were conducted on 7 CNN models and 3 other machine learning (ML) methods. Experimental results show that DRSNet can achieve 78.6% accuracy in debris flow valley classification, which is at least 7.4% higher than common CNN models and 15.2% higher than other ML methods. This article provides new ideas for debris flow susceptibility evaluation. [ABSTRACT FROM AUTHOR]

In map multiscale visualization, typification is the process of replacing original objects, such as buildings, using a smaller number of objects while maintaining initial geometrical and distribution characteristics. During the past few decades, many vector-based methods for building typification have been developed, whereas raster-based methods have received less attention. In this paper, a new method for the typification of buildings with different distribution patterns called superpixel building typification (SUBT) is developed based on raster data. Using this method, buildings with different distribution patterns, such as linear, grid and irregular patterns, are first grouped by image connected component detection and superpixel analysis. Then, the new positions for building typification are determined by superpixel resegmentation. Finally, a new representation of the buildings is determined through analysis of the orientation and shape of the buildings in each superpixel. To test the proposed SUBT method, buildings from both cities and countrysides in China are applied to perform typification. The experimental results show that the proposed SUBT method can realize typification for buildings with linear, grid and irregular distributions while effectively maintaining the original distribution characteristics of the buildings. [ABSTRACT FROM AUTHOR]