Immersive cultural heritage digital documentation and information service for historical figure metaverse: a case of Zhu Xi, Song Dynasty, China.

Cultural heritage is closely linked with individual historical figures, who become a key focus for cultural tourism. Confucianism laid the foundation for much of Chinese civilization, and Confucius and Mencius have been studied extensively and have been influential in many parts of the world. Zhu Xi, the founder and master of Neo-Confucianism (an important part of the Mount Wuyi world cultural heritage), has received less research attention. With the big bang of the metaverse, research on the immersive cultural heritage digital documentation and information service for historical figures has become a new perspective. This paper presents a metaverse-based digital documentation framework for historical figures. This framework addresses the digitization of multimodal data for cultural heritage and adapts to the needs of metaverse presentations. For cultural research and cultural preservation, the paper has explored a combined contactless virtual and real cultural heritage experience system. For tourists, we intend to develop an immersive and holistic cultural tourism information service before, during and after the tour. In particular, we have also developed the Zhu Xi metaverse system. This is a step forward in the construction of a metaverse of historical figures.

A new approach for crop identification with wavelet variance and JM distance.

This paper develops a new crop mapping method through combined utilization of both time and frequency information based on wavelet variance and Jeffries-Matusita (JM) distance (CIWJ for short). A two-dimensional wavelet spectrum was obtained from datasets of daily continuous vegetation indices through a continuous wavelet transform using the Mexican hat and the Morlet mother wavelets. The time-average wavelet variance (TAWV) and the scale-average wavelet variance (SAWV) were then calculated based on the wavelet spectrum of the Mexican hat and the Morlet wavelet, respectively. The class separability based on the JM distance was evaluated to discriminate the proper period or scale range applied. Finally, a procedure for criteria quantification was developed using the TAWV and SAWV as the major metrics, and the similarity between unclassified pixels and established land use/cover types was calculated. The proposed CIWJ method was applied to the middle Hexi Corridor in northwest China using 250-m 8-day composite moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) time series datasets in 2012. The CIWJ method was shown to be efficient in crop field mapping, with an overall accuracy of 83.6 % and kappa coefficient of 0.7009, assessed with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data. Compared with methods utilizing information on either frequency or time, the CIWJ method demonstrates tremendous potential for efficient crop mapping and for further applications. This method could be applied to either coarse or high spatial resolution images for agricultural crop identification, as well as other more general or specific land use classifications.