As the world’s largest developing country, China first proposed the construction of a Beautiful China initiative in 2012, with the aim of exploring Chinese solutions for sustainable regional development. The construction of a beautiful China is based on the guiding ideology of the Five-sphere Integrated Plan in China, that is, the overall plan for building socialism with Chinese characteristics, including economic construction, political construction, cultural construction, social construction and ecological civilization construction. This paper aims to understand the coupling relationship, as well as the spatial and temporal changes, between China’s sustainable development under the Beautiful China initiative and tourism. Using data from the China Statistical Yearbook database, we constructed an evaluation index system to measure both the construction of beautiful China and tourism development using a literature review, statistical analysis, the entropy method and GIS-based spatial analysis methods. Furthermore, using the 31 Chinese provinces as the research subject, we further analyzed the state of Beautiful China construction and tourism development, as well as their coupling relationships of the two systems. Our results show that firstly, the economic “hard power” plays the most prominent role in the process of building a beautiful China under the sustainable development regime, while the status of cultural “soft power” has also been well reflected. Secondly, the weight ranking of tourism evaluation indicators and the spatial distribution of tourism development levels both reflect the central and fundamental role of tourism market demand in tourism development. Third, the weight ranking of tourism evaluation indicators ranks the highest in the mean value of the coupling coordination degree of society, ecology and tourism in the Beautiful China subsystem, which reflects the harmony between society and ecology and the significant livelihood function of tourism as a happiness industry in the new era. Fourth, the spatial and temporal relationship between the coupled and coordinated development of the Beautiful China and tourism systems varies, indicating that there is a regional imbalance in China’s sustainable development. This further indicates the need to adapt to local conditions, and to build on strengths and avoid weaknesses to achieve regional sustainable development. The study highlights China’s contribution to global sustainable development. It also provides theoretical and practical guidance for the promotion of the coordinated development of both Beautiful China and tourism.

The demand for real-time land cover mapping from high-resolution remote sensing (HR-RS) imagery motivates lightweight segmentation models running directly on satellites. By processing on-board and transmitting only fine-grained semantic products instead of massive raw imagery, these models provide timely support for disaster response, environmental monitoring, and precision agriculture. Many recent methods combine convolutional neural networks (CNNs) with Transformers to balance local and global feature modeling, with convolutions as explicit information aggregation modules. Such heterogeneous hybrids may be unnecessary for lightweight models if similar aggregation can be achieved homogeneously, and operator inconsistency complicates optimization and hinders deployment on resource-constrained satellites. Meanwhile, lightweight Transformer components in these architectures often adopt aggressive channel compression and shallow contextual interaction to meet compute budgets, impairing boundary delineation and recognition of small or rare classes. To address this, we propose SatViT-Seg, a lightweight semantic segmentation model with a pure Vision Transformer (ViT) backbone. Unlike CNN-Transformer hybrids, SatViT-Seg adopts a homogeneous two-module design: a Local-Global Aggregation and Distribution (LGAD) module that uses window self-attention for local modeling and dynamically pooled global tokens with linear attention for long-range interaction, and a Bi-dimensional Attentive Feed-Forward Network (FFN) that enhances representation learning by modulating channel and spatial attention. This unified design overcomes common lightweight ViT issues such as channel compression and weak spatial correlation modeling. SatViT-Seg is implemented and evaluated in LuoJiaNET and PyTorch; comparative experiments with existing methods are run in PyTorch with unified training and data preprocessing for fairness, while the LuoJiaNET implementation highlights deployment-oriented efficiency on a graph-compiled runtime. Compared with the strongest baseline, SatViT-Seg improves mIoU by up to 1.81% while maintaining the lowest FLOPs among all methods. These results indicate that homogeneous Transformers offer strong potential for resource-constrained, on-board real-time land cover mapping in satellite missions.

Semantic segmentation of ultra-high-resolution remote sensing (UHR-RS) imagery plays a critical role in land use and land cover analysis, yet it remains computationally intensive due to the enormous input size and high spatial complexity. Existing studies have commonly employed strategies such as patch-wise processing, multi-scale model architectures, lightweight networks, and representation sparsification to reduce resource demands, but they have often struggled to maintain long-range contextual awareness and scalability for inputs of arbitrary size. To address this, we propose RingFormer-Seg, a scalable Vision Transformer framework that enables long-range context learning through multi-device parallelism in UHR-RS image segmentation. RingFormer-Seg decomposes the input into spatial subregions and processes them through a distributed three-stage pipeline. First, the Saliency-Aware Token Filter (STF) selects informative tokens to reduce redundancy. Next, the Efficient Local Context Module (ELCM) enhances intra-region features via memory-efficient attention. Finally, the Cross-Device Context Router (CDCR) exchanges token-level information across devices to capture global dependencies. Fine-grained detail is preserved through the residual integration of unselected tokens, and a hierarchical decoder generates high-resolution segmentation outputs. We conducted extensive experiments on three benchmarks covering UHR-RS images from 2048 × 2048 to 8192 × 8192 pixels. Results show that our framework achieves top segmentation accuracy while significantly improving computational efficiency across the DeepGlobe, Wuhan, and Guangdong datasets. RingFormer-Seg offers a versatile solution for UHR-RS image segmentation and demonstrates potential for practical deployment in nationwide land cover mapping, supporting informed decision-making in land resource management, environmental policy planning, and sustainable development.