Mobile games are becoming a vital medium for social interaction, offering a platform that transcends geographical boundaries.An increasing number of visually impaired individuals are engaging in mobile gaming to connect, collaborate, compete, and build friendships.In China, visually impaired communities face significant social challenges in offline settings, making mobile games a crucial avenue for socialization.However, the design of mobile games and their mapping to real-world environments significantly shape their social gaming experiences.This study explores how visually impaired players in China navigate socialization and integrate into gaming communities.Through interviews with 30 visually impaired players, we found that while mobile games fulfill many of their social needs, technological barriers and insufficient accessibility features, and internal community divisions present significant challenges to their participation.This research sheds light on their social experiences and offers insights for designing more inclusive and accessible mobile games.

As one of the most important application for remote sensing monitoring, land cover mapping has witnessed notable advancements in data acquisition, algorithmic diversity, and classification accuracy. Despite the instrumental role data-driven algorithms have played in the development of global land cover products, their inherent limitations as “black box” methods often fall short of meeting end-users’ specific requirements. In this study, built upon the foundation of the earlier land cover monitoring platform [FROM-GLC plus(FGP)], a data and knowledge dual-driven framework (FGP 2.0) was developed as a user-adaptive framework for intelligent remote sensing land cover mapping. By incorporating ontology-based semantic descriptions with advanced data-driven algorithms, FGP 2.0 provides the capacity for both traditional annual mapping and emerging dynamic mapping. Our results illustrate that FGP 2.0 significantly improves the overall accuracy of annual maps by ~5%, and dynamic maps by ~20% compared to FGP. Moreover, an operational dynamic mapping tool has been developed on the Google Earth engine (GEE), enabling the generation of near-real-time land cover maps for any given place. With an extensible and flexible mapping framework, FGP 2.0 demonstrates the potential of customized land cover monitoring results to suit different application scenarios. This innovative approach not only meets the current demand for reliable annual and dynamic land cover maps but also sets a new benchmark for the integration of geoscientific expertise with machine learning techniques in remote sensing monitoring.