This study aims to construct an integrative model for understanding the factors that shape Chinese tourists’ intentions to visit Thailand as a gastronomic tourism destination. In detail, we investigate the relationships among cognitive experiences, emotional experiences, cultural experiences, affective destination image, cognitive destination image, and the intention to visit Thailand for culinary experiences. Utilizing an online survey method to gather 562 Chinese tourists who have experienced Thai gastronomy, this study continues to use structural equation model to process data. The findings reveal that cognitive, emotional, and cultural experiences significantly influence tourists’ affective and cognitive destination images, positively impacting their intention to visit Thailand for its culinary offerings. The affective and cognitive destination images act as crucial mediators, intricately linking these experiences with travel intentions. This approach improves our understanding of the dynamics involved. It also provides practical insights for developing targeted marketing strategies.

Tourism city brand image construction is a strategic measure to enhance the city’s core competitiveness, and has received great attention from various tourism cities. As a new force in promoting urban development, local youth must accurately grasp their perception of the city’s brand image, to realize the simultaneous development of youth development and urban development, and the integrated development of youth, industry, and city. This paper focuses on the city brand building of tourist cities among local youth, adopts the brand association measurement tool “brand concept map”, takes Chongqing, a tourist city in China, as the field, and the local youth as the research object, and establishes the analysis perspective of the correlation strength of results based on traditional methods. Based on exploring the characteristics of brand image perception, this paper further explores the formation mechanism behind the characteristics from the perspective of the diversity of local youth’s perception channels of city brand image.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.

Objective: to achieve accurately and rapidly the mapping of agricultural land use and crop distribution at the township scale. Methods: this study, based on specific methods, such as, time-series remote sensing index threshold classification and maximum likelihood, classifies each land use type and extracts crop spatial information, under the guidance of Sentinel-2A remote sensing images, to carry out agricultural land use mapping at township scale. And the mapping concerned will be verified by comparing with an agricultural spatial information map of a 0.5 m resolution, which is based on WorldVieW-2 fused images. Results: (1) the area accuracy of grain and oil crop land, vegetable land, agricultural facilities land and garden land is fairly good, with 92.93%, 98.98%, 95.71% and 95.14% respectively, and within 8% variation from actual area; (2) the spatial information of plot boundary, farmland road network, and canal network produced by OSM road data and historical high-resolution images was overlayed with the classification results of Sentinel-2A multi-spectral image for mapping, which can improve the accuracy of plot boundary information of classification results for the image with 10 m resolution. Conclusions: the use of multi-source information fusion method, agricultural land use and crop distribution space big data produced by Sentinel-2A optical image, can effectively improve the accuracy and timeliness of land use mapping at the township scale, to provide technical reference for the application of remote sensing big data to carry out agricultural landscape analysis at the township scale, optimization and adjustment of agricultural structure, etc.