Air pollution, including particulate matter (PM_2.5) pollution, is extremely harmful to the environment as well as human health. The Beijing–Tianjin–Hebei (BTH) Region has experienced heavy PM_2.5 pollution within China. In this study, a six-year time series (January 2013–December 2018) of PM_2.5 mass concentration data from 102 air quality monitoring stations were studied to understand the spatio-temporal variation characteristics of the BTH region. The average annual PM_2.5 mass concentration in the BTH region decreased from 98.9 μg/m³ in 2013 to 64.9 μg/m³ in 2017. Therefore, China has achieved its Air Pollution Prevention and Control Plan goal of reducing the concentration of fine particulate matter in the BTH region by 25% by 2017. The PM_2.5 pollution in BTH plain areas showed a more significant change than mountains areas, with the highest PM_2.5 mass concentration in winter and the lowest in summer. The results of spatial autocorrelation and cluster analyses showed that the PM_2.5 mass concentration in the BTH region from 2013–2018 showed a significant spatial agglomeration, and that spatial distribution characteristics were high in the south and low in the north. Changes in PM_2.5 mass concentration in the BTH region were affected by both socio-economic factors and meteorological factors. Our results can provide a point of reference for making PM_2.5 pollution control decisions.

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.

The structure from motion (SfM) method has achieved great success in 3D sparse reconstruction, but it still faces serious challenges in large-scale scenes. Existing hybrid SfM methods usually do not fully consider the compactness between images and the connectivity between subclusters, resulting in a loose spatial distribution of images within subclusters, unbalanced connectivity between subclusters, and poor robustness in the merging stage. In this paper, an efficient and robust hybrid SfM method is proposed. First, the multifactor joint scene partition measure and the preassignment balanced image expansion algorithm among subclusters are constructed, which effectively solves the loose spatial distribution of images in subclusters problem and improves the degree of connection among subclusters. Second, the global GlobalACSfM method is used to complete the local sparse reconstruction of the subclusters under the cluster parallel framework. Then, a decentralized dynamic merging rule considering the connectivity of subclusters is proposed to realize robust merging among subclusters. Finally, public datasets and oblique photography datasets are used for experimental verification. The results show that the method proposed in this paper is superior to the state-of-the-art methods in terms of accuracy and robustness and has good feasibility and advancement prospects.

Garlic is one of the main economic crops in China. Accurate and timely extraction of the garlic planting area is critical for adjusting the agricultural planting structure and implementing rural policy actions. Crop extraction methods based on remote sensing usually use spectral–temporal features. Still, for garlic extraction, most methods simply combine all multi-temporal images. There has been a lack of research on each band’s function in each multi-temporal image and optimal bands combination. To systematically explore the potential of the multi-temporal method for garlic extraction, we obtained a series of Sentinel-2 images in the whole garlic growth cycle. The importance of each band in all these images was ranked by the random forest (RF) method. According to the importance score of each band, eight different multi-temporal combination schemes were designed. The RF classifier was employed to extract garlic planting area, and the accuracy of the eight schemes was compared. The results show that (1) the Scheme VI (the top 39 bands in importance score) achieved the best accuracy of 98.65%, which is 6% higher than the optimal mono-temporal (February, wintering period) result, and (2) the red-edge band and the shortwave-infrared band played an essential role in accurate garlic extraction. This study gives inspiration in selecting the remotely sensed data source, the band, and phenology for accurately extracting garlic planting area, which could be transferred to other sites with larger areas and similar agriculture structures.

Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms.

Water pipeline leak detection in a fast and accurate way is of much importance for water utility companies and the general public. At present, the rapid development of remote sensing and computer technologies makes it possible to detect water pipeline leaks on a large scale efficiently and timely. The leakage will cause an increase in the water content and dielectric constant of the soil around the pipeline, so it is feasible to determine the leakage site by measuring the subsurface soil relative dielectric constant (SSRDC). In this paper, we combine the SAOCOM-1A L-band synthetic-aperture radar (SAR) and the ground-penetrating radar (GPR) data to develop regression models that predict the SSRDC values. The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. We evaluate multiple linear regression (MLR), random forest (RF), and multi-layer perceptron neural network (MLPNN) models for their ability to predict the SSRDC values using the selected features. The experimental results show that the MLPNN model ($R^2$ = 0.705, RMSE = 1.936, MAE = 1.664) can better estimate the SSRDC values. Further, in the main urban area of Tianjin, China, which has a large water pipeline system, the SSDRC values of the area are obtained with the best model, and the locations where the predicted SSDRC values exceeded a certain threshold were considered potential leak locations. The empirical results indicate an encouraging potential of the proposed method to locate the pipeline leaks. This will provide a new avenue for the monitoring and treatment of water pipeline leaks.