The detection of traffic objects in aerial scenes holds significant application potential in both military and civilian sectors. However, current aerial traffic object detection techniques based on computer vision face challenges including limited awareness of object direction, a heavy computational burden on the feature extraction backbone network, and inadequate capacity to learn crucial semantic information. In this paper, our focus is on investigating the mechanisms for predicting the directional perception of traffic objects in aerial scenes, achieving backbone network lightness, and exploring methods for extracting key semantic information from objects. Firstly, to tackle the challenge of poor perception of traffic object direction and angle in aerial scenes, we utilize techniques like equivariant vector field convolution, multi-task anchor-free prediction, and adaptive loss to develop a precise mechanism for recognizing and predicting object directions. Secondly, given the presence of small-sized and numerous objects in aerial scenes, we propose the adoption of a lightweight backbone network employing channel stacking to decrease the model’s computational burden. Additionally, we establish a theoretical framework and methodology for optimizing and compressing this backbone network, aimed at enhancing feature extraction and propagation for aerial traffic objects. Furthermore, to address the issue of inadequate learning of key semantic information features, we incorporate saliency attention and multi-scale contextual information to capture the essential semantic characteristics of the objects. We also establish a method for extracting semantic features specifically for aerial traffic objects. The approach presented in this paper broadens the applicability of aerial object detection algorithms and offers novel methodologies and theoretical foundations for object detection in intricate scenarios.

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of “double carbon”. In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB.

With economic progression in China, Yellow River Basin serves as a critical economic belt, which has also been recognized as a cradle of Chinese culture. A watershed is a complex structure of social, economic, and natural factors, and the diversity of its components determines its complexity. Studies on the spatiotemporal distribution characteristics and factors influencing the tourism eco-efficiency at the watershed scale are crucial for the sustainable regional socio-economic development, maintaining a virtuous cycle of various ecosystems, and comprehensively considering the utilization and coordinated development of various elements. Based on tourism eco-efficiency, the coordination degree of regional human–land system and the sustainable development levels can be accurately measured. With the tourism eco-efficiency in the Yellow River Basin from 2009 to 2019, the present study considers 63 cities in the Yellow River Basin as the research area by adopting the super-efficiency slacks-based measure (Super-SBM) model. Methods such as trend surface analysis, spatial autocorrelation analysis, elliptic standard deviation analysis, and hot spot analysis were used to explore their spatiotemporal distribution and evolution characteristics. The geographical and temporal weighted regression (GTWR) model was used to determine the factors influencing the tourism eco-efficiency value. The findings are as follows: ①The level of tourism eco-efficiency in the Yellow River Basin is not high, exhibiting a fluctuating upward trend. ②The tourism eco-efficiency in the Yellow River Basin shows significant spatial interdependence and agglomeration. Furthermore, the track of the center of gravity moves from northeast to southwest. ③ The tourism eco-efficiency in the Yellow River Basin is affected by various factors, with the economic development level having the greatest influence.

In this paper, we propose a novel video-sharing strategy based on the joint estimation of social influence and sharing capacity in wireless networks (SSISC), which promotes the scale and efficiency of video spread and ensures the balance of supply and demand. SSISC designs an estimation model of video-sharing gains by investigating social influence levels, sharing capacities (including capacities of information dispatching and video delivery), and predicted expansion scale. Some social parameters (e.g., centrality of degree and betweenness, and average shortest distance) and some parameters of sharing performance (e.g., number of forwarded messages and cached videos, the average time of transmission and freeze) are used to evaluate social influence, capacities of information dispatching, and video delivery; video interest levels, social relationship levels, and historical push success rates are used to predict video proliferation scale. A video-spreading strategy based on the assistance of spread nodes is designed, which controls the process of video push according to available bandwidth and push priority to balance supply and demand and ensure user experience quality. Extensive tests show how SSISC achieves much better performance results in comparison with other state-of-the-art solutions.

The video services that account for the majority of global network traffic consume significant amounts of electricity and network resources to meet the large-scale demand of users. Variations in user interest and social influence lead to high maintenance costs for achieving a dynamic balance between supply and demand, which negatively impacts the sustainable development of video services. In this paper, we propose a social-aware video-sharing solution using demand prediction of epidemic-based propagation in wireless networks (SDPEP). SDPEP constructs a video propagation model based on user “pull” and “push” sharing behaviors and designs an estimation method for calculating the probability of video fetching by investigating user interests and social relationships. SDPEP uses the probability of video fetching to calculate the basic reproduction number during epidemic-based video propagation, predicting user demand during the propagation process. To ensure efficient caching with low-cost adjustments to video distribution, SDPEP employs a caching-based adjustment strategy for distributing videos while maintaining dynamic balance between supply and demand. Extensive testing shows that SDPEP outperforms other state-of-the-art solutions.

Rice sustains 18% of the global population on merely 7% of arable land, making accurate rice mapping critical to ensure food security in China. Synthetic Aperture Radar (SAR), with minimal weather dependence, enables continuous rice monitoring in cloud-prone regions. However, existing SAR-based methods require prior phenological knowledge or extensive training samples, limiting their scalability. To address this limitation, we propose Rice-SamPheno—an automated method utilizing time-series Sentinel-1 data, generating rice samples with implicit phenological information. This method characterizes rice backscatter dynamics across growth stages, particularly capturing backscatter variations across geographical regions. The dynamic threshold (DT) algorithm—our core innovation—enables a breakthrough in rice sample generation through self-adaptation to regional agronomic variations including transplanting timing, growth duration, and single/double rice rotation patterns. This process produces spatially explicit, phenology-labeled samples at large scale, with the combination of spectral similarity measures (SSM) and adaptive optimal threshold estimation dynamically refining rice classification thresholds. Validation across three agroecological zones demonstrated post-masking accuracies of 95.78% (Taishan, Guangdong), 90.68% (Nanchang, Jiangxi), and 94.87% (Fujin, Heilongjiang). Pre-masking results (85.94% in Jiangxi) revealed that aquatic vegetation interference was mitigated by farmland masking (Esri 2020 dataset). Crucially, algorithmically generated samples covering heterogeneous cropping systems enabled 3.53% higher rice mapping accuracy than field surveys in Jiangxi. The adaptive optimal threshold method achieved near-identical accuracy (<0.2% deviation) to manual thresholds, while overcoming region-specific limitations. These advancements establish Rice-SamPheno as a scalable solution for cloud-affected regions, offering phenological priors and high-confidence training samples for global rice monitoring in data-scarce environments.