DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation.

Human pose estimation is the basis of many downstream tasks, such as motor intervention, behavior understanding, and human-computer interaction. The existing human pose estimation methods rely too much on the similarity of keypoints at the image feature level, which is vulnerable to three problems: object occlusion, keypoints ghost, and neighbor pose interference. We propose a dual-space-driven topology model for the human pose estimation task.

A Joint Encryption and Compression Algorithm for Multiband Remote Sensing Image Transmission.

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with "Belt and Road" countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security.

The short and long-term impact of nonpharmaceutical interventions on the prevalence of varicella in Xi'an during the COVID-19 pandemic.

Varicella is a highly prevalent infectious disease with a similar transmission pathway to coronavirus disease 2019 (COVID-19). In the context of the COVID-19 pandemic, anti-COVID-19 nonpharmaceutical interventions (NPIs) have been implemented to prevent the spread of the infection. This study aims to analyze varicella's epidemiological characteristics and further investigate the effect of anti-COVID-19 NPIs on varicella in Xi'an, northwestern China.

Assessing urban wetlands dynamics in Wuhan and Nanchang, China.

Urban wetlands play a crucial role in sustainable social development. However, current research mainly focuses on specific wetland types, and fine extraction of urban wetlands remains a challenge. This study proposes a fine extraction framework based on hierarchical decision trees and shape features for urban wetlands, using Sentinel-2 remote sensing data to obtain detailed wetland data of Wuhan and Nanchang from 2016 to 2022.

Spatiotemporal Patterns of the Omicron Wave of COVID-19 in the United States.

COVID-19 has undergone multiple mutations, with the Omicron variant proving to be highly contagious and rapidly spreading across many countries. The United States was severely hit by the Omicron variant. However, it was still unclear how Omicron transferred across the United States.

Soil moisture dominates the variation of gross primary productivity during hot drought in drylands.

The frequency and severity of hot drought will increase in the future due to impact of climate change and human activities, threatening the sustainability of terrestrial ecosystems and human societies. Hot drought is a typical type of drought event, high vapor pressure deficit (VPD) and low soil moisture (SM) are its main characteristics of hot drought, with increasing water stress on vegetation and exacerbating hydrological drought and ecosystem risks. However, our understanding of the effects of high VPD and low SM on vegetation productivity is limited, because these two variables are strongly coupled and influenced by other climatic drivers.

Data-driven estimation of nitric oxide emissions from global soils based on dominant vegetation covers.

Soils are a major source of global nitric oxide (NO) emissions. However, estimates of soil NO emissions have large uncertainties due to limited observations and multifactorial impacts. Here, we mapped global soil NO emissions, integrating 1356 in-situ NO observations from globally distributed sites with high-resolution climate, soil, and management practice data.

Processing toxic metal source proxies appropriately for better spatial heterogeneity source apportionment.

Soil toxic metals have strong spatial heterogeneity, and their sources vary among regions. Thus, this study integrated the Catreg and geographically weighted regression (GWR) models to quantitatively extract the main source proxies (numerical and categorical variables were analyzed simultaneously) for different toxic metals and analyze the spatial heterogeneity of the distributions of these sources. Pb, Cd and Hg were the predominant toxic metals in soil.

Nighttime light perspective in urban resilience assessment and spatiotemporal impact of COVID-19 from January to June 2022 in mainland China.

The outbreak of the coronavirus disease 2019 (COVID-19) epidemic has resulted in large threats and damage to society and the economy. In this study, we evaluate and verify the comprehensive resilience and spatiotemporal impact of the COVID-19 epidemic from January to June 2022 in mainland China based on multisource data. First, we adopt a combination of the mandatory determination method and the coefficient of variation method to determine the weight of the urban resilience assessment index.

Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models.

The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth and site-specific crop management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) and its simplified version and compared them for estimating AGB. These methods estimate the AGB of various rice cultivars throughout the growing season by integrating vegetation index (VI) and canopy height based on images acquired by unmanned aerial vehicles (UAV).

72-hour real-time forecasting of ambient PM by hybrid graph deep neural network with aggregated neighborhood spatiotemporal information.

The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM forecasting over the whole Beijing-Tianjin-Hebei region (overall R increases from 0.

Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan.

Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (V).

An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak.

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation.

Automatic rape flower cluster counting method based on low-cost labelling and UAV-RGB images.

Background: The flowering period is a critical time for the growth of rape plants. Counting rape flower clusters can help farmers to predict the yield information of the corresponding rape fields. However, counting in-field is a time-consuming and labor-intensive task.

Few-shot remote sensing image scene classification based on multiscale covariance metric network (MCMNet).

Few-shot learning (FSL) is a paradigm that simulates the fast learning ability of human beings, which can learn the feature differences between two groups of small-scale samples with common label space, and the label space of the training set and the test set is not repeated. By this way, it can quickly identify the categories of the unseen image in the test set. This method is widely used in image scene recognition, and it is expected to overcome difficulties of scarce annotated samples in remote sensing (RS).

Impact of global urban expansion on the terrestrial vegetation carbon sequestration capacity.

Continuous urban expansion has a negative impact on the potential of terrestrial vegetation. Till now, the mechanism of such impact remains unclear, and there have been no systematic investigations. In this study, we design a theoretical framework by laterally bridging urban boundaries to explain the distress of regional disparities and longitudinally quantify the impacts of urban expansion on net ecosystem productivity (NEP).

How do people find pairs?

Humans continuously scan their visual environment for relevant information. Such visual search behavior has typically been studied with tasks in which the search goal is constant and well-defined, requiring relatively little interplay between memory and orienting. Here we studied a situation in which the target is not known in advance, and instead, memory needs to be dynamically updated during the actual search.

Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory.

Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.

Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000-2018.

Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.

Influences of environmental factors on the dissipation of green tides in the Yellow Sea, China.

Green tides attack the Yellow Sea every year since 2007 and have caused substantial financial loss. Based on Haiyang-1C/Coastal zone imager (HY-1C/CZI) and Terra/MODIS satellite images, the temporal and spatial distribution of green tides floating in the Yellow Sea during 2019 was extracted. The relationships between the growth rate of the green tides and the environmental factors including sea surface temperature (SST), photosynthetically active radiation (PAR), sea surface salinity (SSS), nitrate and phosphate during the green tides' dissipation phase has been detected.