In the modern metropolis, the coverage of community hospitals is getting larger and larger. But high-level hospitals treating serious and sudden illness are still kind of scarce social resources. Shenzhen is the forefront of China's reform and opening up, its solution to the problem of medical allocation can be used as a reference for other cities. In this paper, we assume that primary, secondary and tertiary hospitals are main places for treating major diseases in Shenzhen, and analyze each sub-districts' spatial accessibility to them based on travel time and spatial difference within the city. A kernel density two-step floating catchment area method (KD2SFCA) is used to calculate how many medical resources each sub-district could share, in which the travel time from residential district to hospitals is used as an important parameter in evaluating the accessibility between suppliers and demanders. According to statistics of travel time by both driving and public transportation, the impedance function in KD2SFCA is modified and actual data of Shenzhen is used to fit its parameters, which could better simulate the attenuation trend of hospital service capabilities over the travel time. The spatial accessibilities are calculated under different travel modes and multiple time thresholds, and then spatial autocorrelation analysis method is used to analyze the spatial correlation of residents' accessibility to these medical resources. From both analysis of spatial accessibility and spatial autocorrelation, distinct variations in spatial distribution of high-level hospitals could be observed: of which the south and central parts of Shenzhen have significantly higher accessibilities, while the eastern and western regions are relatively lower. In particular, 12 sub-districts gain quite lower scores than others, showing that constructions on high-level hospitals should be strengthened further. In conclusion, the spatial configuration of high level hospitals in Shenzhen is not well balanced, Further optimization is urgently needed

This paper studies the use of the Fully Convolutional Networks (FCN) model in the extraction of water bodies from Very High spatial Resolution (VHR) optical images in the case of limited training samples. Two different seasonal GaoFen-2 images with a spatial resolution of 0.8 m in the south of the Beijing metropolitan area were used to extensively validate the FCN model. Four key factors including input features, training data, transfer learning, and data augmentation related to the performance of the FCN model were empirically analyzed by using 36 combinations of various parameter settings. Our findings indicate that the FCN-based method can work as a robust and cost-effective tool in the extraction of water bodies from VHR images. The FCN-based method trained on a small amount of labeled L1A data can also significantly outperform the Normalized Difference Water Index (NDWI) based method, the Support Vector Machine (SVM) based method, and the Sparsity Model (SM) based method, even when radiometric normalization and spatial contexts are introduced to preprocess the input data for the latter three methods. The advantages of the FCN-based method are mainly due to its capability to exploit spatial contexts in the image, especially in urban areas with mixed water and shadows. Though the settings of four key factors significantly affect the performance of the FCN based method, choosing a qualified setting for the FCN model is not difficult. Our lessons learned from the successful use of the FCN model for the extraction of water from VHR images can be extended to extract other land covers.

Monitoring the quality of the urban ecological environment has become one of the important elements of promoting a sustainable urban development. The remote sensing ecological index (RSEI) provides a new research direction in urban ecological environment monitoring, combined with remote sensing. However, by using the principal component analysis method in RSEI, the calculation results are complicated and the workload is huge. To effectively assess the urban ecological environment, an improved remote sensing ecological index (IRSEI) was created to improve the ease of data use by using the entropy weighting method with spatiotemporal characteristics and seasonal variations. Furthermore, a geographically weighted regression model was used to quantify the impact of human activities on the urban ecological environment quality. The results showed that the IRSEI could provide a new method for monitoring the urban ecological environment quality, which makes the work easier while ensuring the validity of the results. It was concluded that (1) seasonal differences in the ecological quality of the study area were evident in the IRSEI model and the overall ecological environment quality of Jining City had been on an upward trend in the past 20 years; (2) the ecological quality in the study area was unevenly distributed spatially, with the southwestern part being better than the northeastern part, and the ecological grade being predominantly between moderate and good; and (3) the spatial aggregation effect of the IRSEI was increasing with time. The geographically weighted regression (GWR) revealed the influence of human activities on the ecological environment quality, among which economic level was positively related to ecological improvement, but the population density and night light index were negatively related to improvements in the ecological environment; road network density only showed a negative correlation in 2020. As Jining urbanizes, attention should be paid to protecting the built environment and population distribution.

The collection of traditional administrative unit-based gross domestic product (GDP) data is time-consuming and laborious, and the data lacks accurate spatial information. Long-term series nighttime light (NTL) data can provide effective spatiotemporal GDP change information, which can be used to analyze economies’ spatial distributions and development trends. In this study, we generated a spatial model of the relationship between NTL indices and GDP parameters, based on NPP-VIIRS-like NTL data for the period 2001 to 2020, conducted a multitemporal and multilevel connectivity analysis of the GDP spatialization data, and constructed a tree structure for horizontal and vertical analysis. Standard deviation ellipses and economic centers of the first-level economic connected components at the provincial and municipal levels were generated, and the economic center distribution range and development direction at the provincial and municipal levels were analyzed. The results show that GDP spatialization data, based on NPP-VIIRS-like NTL data, can intuitively reflect the GDP spatial distribution. In Henan Province, the economic levels of different regions vary, and the economic regions represented by Zhengzhou have developed rapidly, driving surrounding regional economic rapid development. Henan Province’s development trend from single-city economic centers to multicity economic centers is obvious, and the economic center has shifted to the southeast.

Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments.

Among tasks related to intelligent interpretation of remote sensing data, scene classification mainly focuses on the holistic information of the entire scene. Compared to pixel-level or object-based tasks, it involves a richer semantic context, making it more challenging. With the rapid advancement of deep learning, convolutional neural networks (CNNs) have found widespread applications across various domains, and some work has introduced them into scene classification tasks. However, traditional convolution operations involve sliding small convolutional kernels across an image, primarily focusing on local details within a small receptive field. To achieve better modeling of the entire image, the smaller receptive field limits the ability of convolution operation to capture features over a broader range. To this end, we introduce a large kernel CNNs into the scene classification task to expand the receptive field of the mode, which allows us to capture comprehensive non-local information while still acquiring rich local details. However, in addition to encoding spatial association, the effective information within the feature maps is also strongly channel-related. Therefore, to fully model this channel dependency, a novel channel separation and mixing module has been designed to realize feature correlation in the channel dimension. The combination of them forms a Large Kernel Separable Mixed Convnet (LSMNet), enabling the model to capture effective dependencies of feature maps in both spatial and channel dimensions, thus achieving enhanced feature expression. Extensive experiments conducted on three datasets have also validated the effectiveness of the proposed method.