With the gradual adjustment of urban expansion and discontinuation of non-essential functions in Beijing, many decades-old buildings have been demolished. Thus, construction and demolition waste (CDW) has become the focus of urban and dust pollution management. However, CDW piles are volatile and present irregular boundaries. Therefore, it is essential to map CDW regions in a timely and accurate manner to achieve urban development while protecting the environment. To address this issue, we proposed a method of CDW identification based on change detection and deep learning. First, ZY-3 multispectral images from 2016 and 2019 and their difference images were used for initial sample preparation. We expanded the samples using the post-classification comparison method of change detection, resulting in a 25.4% increase in the valid sample set. The expanded samples were then used as an input to the DeepLabV3+ for training. Thereafter, combined with the change information from the digital elevation model, specific forms, such as demolition remains, landfill CDW, and large-scale dump, were extracted using spatial analysis methods. The overall accuracy of CDW recognition was 91.67%, with a Kappa coefficient of 0.8642. In addition, we calculated the accuracy indices using only the initial samples, obtaining a mean Intersection-over-Union value that was 0.086 lower than that obtained using the expanded sample set. Similar results were obtained in PSPNet and UNet. This suggests that change detection is useful in improving the accuracy of the deep learning models. This study is the first to identify three existing forms of CDW and can effectively address the misclassification between CDW and bare land to identify CDW efficiently.

Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM_2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM_2.5, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) during 2017–2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM_2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017–2021, except for a slight rebound (3.6%) in NO₂ concentration in 2021 in Beijing. Notably, the O₃ concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM_2.5, NO₂, SO₂, and CO in February of 2020, but PM_2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM_2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM_2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM_2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.