In current clinical practice, the localization of human acupuncture points relies extensively on the subjective experience of physicians. Therefore, despite being a crucial basic content of traditional Chinese medicine (TCM), acupuncture point localization has not been well expanded and promoted through intelligent means. Our goal is to explore an efficient and reliable solution for acupuncture point localization and recognition that addresses the shortcomings of subjectivity and standardization in this task. We focus on the weak feature body surface of the human back and propose an innovative approach that utilizes a deep learning network with a self-attention module for global extraction of image features. This methodology differs from common Convolutional Neural Networks (CNNs) which often lead to classification ambiguous in weak feature image tasks due to excessive cropping and scaling operations during feature extraction. Moreover, our self-constructed dataset of human back acupuncture points provides data support for model training. The localization task for the back acupuncture points of the subjects in the dataset strictly follows the national standard definition and is labelled by professional doctors of TCM to ensure data robustness and quality. Our preliminary experiments validate that our proposed network learns higher-quality global image features, achieving an average accuracy of less than 1cm in the localization and recognition task of 84 acupuncture points on the back of the human body.

In China, for many general colleges and universities, the postgraduate programs related to remote sensing are the subdisciplines named Photogrammetry and Remote Sensing, and Geographical Environment Remote Sensing. Totally, there are 125 and 67 colleges and universities which offer the related programs with the master's and doctoral degrees, respectively. In this paper, we introduce the objective and requirement, as well as the training mode of the related postgraudate programs. After years of development, the remote sensing education in China has ranked among the best in the world. In the future, encouraged by a series of policies and measures of the Ministry of Education of China, the remote sensing education will develop faster and better, and make greater contributions to China and the world.

In China, Remote Sensing Science and Technology (RSST) is a young undergraduate program offered by the colleges and universities since 2001. As of 2018, there are as many as 45 colleges and universities authorized to offer the programs and to train the undergraduate students in the field of remote sensing and related applications. To ensure the smooth implementation of the undergraduate education, the Ministry of Education of the People's Republic of China has issued a series of policies and measures, including the program objective, requirements, and main curriculum structure. In Jiangsu Normal University, following the relevant policies and regulations, the students in the RSST program are trained and classified as three modules: innovative, compound and international talents, to meet the kinds of interests of the students and the different needs of employers.