Exploring the spatiotemporal characteristics of park visitors and the “push and pull” factors that shape this mobility is critical to designing and managing urban parks to meet the demands of rapid urbanization. In this paper, 56 parks in Shenzhen were studied in 2019. First, cell phone signaling data were used to extract information on visitors’ departure locations and destination parks. Second, the bivariate Moran’s I and bivariate local Moran’s I (BiLISA) methods were used to identify the statistical correlation between the factors of the built environment and the park recreation trips. Finally, linear regression models were constructed to quantify the factors influencing the attractiveness of the park. Our study showed the following: (1) Recreation visitors at large parks varied significantly among population subgroups. Compared with younger adults, teenagers and older adults traveled lower distances and made fewer trips, and in particular, older adults of different genders differed significantly in park participation. (2) Recreational trips in large parks were related to the functional layout of the built environment around their residence. In areas with rich urban functions (e.g., southern Shenzhen), trips to large parks for leisure are more aggregated. (3) The findings reinforce the evidence that remote sensing data for urban vegetation can be an effective factor in characterizing park attractiveness, but the explanatory power of different vegetation data varies widely. Our study integrated the complementary human activity and remote sensing data to provide a more comprehensive understanding of urban park use and preferences. This will be important for future park planning.

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.

This study aims to employ numerical simulations to understand the dynamics of wind fields and air pollutant dispersion in the proximity of a nuclear plant, situated within a specified urban environment. By leveraging computational fluid dynamics (CFD) combined with geographical information system (GIS) data, the research comprehensively models atmospheric interactions in terms of wind flow patterns, building-induced pressure variances, and pollutant trajectories. The computational domain extends over an area of $$8.8\,\textrm{km} \times 8.4\,\textrm{km}$$ 8.8 km × 8.4 km , vertically stretching to 0.5 km. The wind and pollutant distribution equations are discretized using the finite volume method, providing detailed insights into fluid interactions with urban topographies. Key findings highlight the profound influences of terrain, urban structures, and wind flow behavior on the dispersion of radioactive aerosols, shedding light on potential risks and safety protocols for nuclear plant environments.

The identification of hazardous materials is a key measure in the prevention and control of fire and explosion disasters. Conventional techniques used to identify hazardous materials include contact detection and post-sampling laboratory testing, which cannot meet the needs of extreme environments, where personnel and equipment are not accessible for on-site detection. To address this problem, this paper proposes a method for the classification and identification of hazardous materials based on convolutional neural networks, which can achieve non-contact remote detection of hazardous materials. Firstly, a dataset containing 1800 hyperspectral images of hazardous materials, which can be used for deep learning, is constructed based on the hazardous materials hyperspectral data cube. Secondly, based on this, an improved ResNet50-based classification method for hazardous materials is proposed, which innovatively utilizes a classification network based on offset sampling convolution and split context-gated convolution. The results show that the method can achieve 93.9% classification accuracy for hazardous materials, which is 1% better than the classification accuracy of the original ResNet50 network. The network also has high performance under small data volume conditions, effectively solving the problem of low classification accuracy due to small data volume and blurred image data features of labelled hazardous material images. In addition, it was found that offset sampling convolution and split context-gated convolution showed synergistic effects in improving the performance of the network.