The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China’s epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country’s total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.

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Solid waste detection is of great significance for environmental protection. In recent years, object detection methods based on deep learning have progressed rapidly. However, it is often extremely difficult to collect sufficient data to train a model with a good performance. In this study, a data augmentation strategy was introduced to generate sufficient synthetic high-quality images for solid waste detection. First, a modified pix2pix model was proposed, in which a local-global discriminator (LGD) was designed to improve the detailed and global information of the generated images, which are commonly fuzzy with the original pix2pix model. Second, a copy-paste operator was utilized, which simply pastes the bounding box of the generated objects into different images to enhance the diversity of the samples. In this manner, the expanded dataset can be utilized to train different object detection models, for which FPN and Yolo-v4 were introduced as the validation models in this study. The experimental results show that the proposed strategy outperforms the traditional pix2pix method and the generated synthetic images can effectively improve the performance of object detection methods.

Using a shape model (SM) is a typical method to determine the phenological phases of crops with long-time-series satellite remote sensing data. The average AGDD-based shape model (AAGDD-SM) takes temperature into account compared to SM, however, the commonly used daily average temperature is not sufficient to determine the exact AGDD owing to the possibly significant changes in temperatures throughout the day. In this paper, a modified shape model was proposed for the better estimation of phenological dates and it is incorporated into the continuous AGDD (CAGDD) which was calculated based on temperatures from a continuous 24 h within a day, different from the calendar day or the average AGDD indicators. In this study, the CAGDD replaced the abscissa of the NDVI growth curve over a 5-year period (2014 to 2018, excluding 2015) for a test site of early rice in Jiangxi province of China. Four key phenological phases, including the reviving, tillering, heading and anthesis phases, were selected and determined with reference to the field-observed phenological data. The results show that compared with the AAGDD-SM, the method proposed in this paper has basically improved the prediction of each phenological period. For those cases where the average temperature is lower than the minimum temperatures (K1) but the effective accumulated temperature is not zero, more accurate AGDD can be calculated according to the method in this paper.

The COVID-19 pandemic has been a great challenge to society, the economy, and population health. It has become a significant public health event and social problem. Exploring the impact of COVID-19 on the accessibility of outdoor sports venues is crucial for people’s health. Based on spatial theory, the quantitative and qualitative analyses of outdoor sports venues’ spatial distribution and accessibility were conducted, and the epidemic’s impact on them was analyzed. The results show that: (1) The existing outdoor sports venues in Nanchang show a distribution pattern of “sparse in the north and south, and strong aggregation in the middle”. (2) As a result of the epidemic, the center of the standard deviation ellipse in outdoor sports sites shifted to the southeast, while the number of open venues decreased by 68%. (3) Before COVID-19, the entire study area could achieve full coverage by driving for 17 min, riding for 70 min, or walking for 119 min. After COVID-19, the time increased to 29, 109, and 193 min, respectively. (4) Under the high-risk scenario of COVID-19, the average walking time for people to reach outdoor sports venues increased from 6.2 min to 14.0 min in the study area, with an increase of 126%. Finally, according to the findings of this study, recommendations were made on how government departments could build or re-open outdoor sports venues during and after this epidemic.