UAVs, also known as unmanned aerial vehicles, have emerged as an efficient and flexible system for offering a rapid and cost-effective solution. In recent years, large-scale mapping using UAV photogrammetry has gained significant popularity and has been widely adopted in academia as well as the private sector. This study aims to investigate the technical aspects of this field, provide insights into the procedural steps involved, and present a case study conducted in Cesme, Izmir. The findings derived from the case study are thoroughly discussed, and the potential applications of UAV photogrammetry in large-scale mapping are examined. The study area is divided into 12 blocks. The flight plans and the distribution of ground control point (GCP) locations were determined based on these blocks. As a result of the data processing procedure, average GCP positional errors ranging from 1 to 18 cm have been obtained for the blocks.

Facing the digital economy era, considerable attention is paid to the importance of understanding the fundamental impact on the information and development of blended teaching methods regarding the higher education. For this reason, the purpose of this study is to answer the challenges brought by the digital economy era, identify the effective teaching methods which would be used in English Correspondence course in the era of digital economy, aiming to form the patterns of learning, provide high motivation, strength and knowledge, and most importantly contribute to the complex competences of future working. For further research, it is expected to be able to prove that using the blended teaching methods will effectively improve students’ communication skills and learning efficiency, enhance students’ learning experience and critical thinking skills.

There are diverse effects in consequence of exposure to radiofrequency electromagnetic fields (RF-EMF). The interactions of fields and the exposed body tissues are related to the nature of exposure, tissue comportment, field strength and signal frequency. These interactions can crop different effects.

Under the background of green economic transformation, the sustainable utilization of ecological resources has become a trend, and bamboo all-for-one tourism has become a new development direction for bamboo-resource-rich areas. Based on the all-for-one tourism model and characteristics of bamboo resources, this paper puts forward a bamboo all-for-one tourism model, which shows the relationship between resources, products, and markets, and elaborates on the joint effect mechanism of industrial environment, governance environment, and external environment. Taking Yibin City, Sichuan Province as an example, this paper also analyzes existing problems of developing bamboo all-for-one tourism and then proposes suggestions to provide effective analytical ideas and reference, such as establishing a market-oriented all-product development model, introducing the sustainable development concept of bamboo management, establishing the management concept of sharing by all people, and driving all industries developing in a coordinated way.

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%.