Power line extraction is the basic task of power line inspection with unmanned aerial vehicle (UAV) images. However, due to the complex backgrounds and limited characteristics, power line extraction from images is a difficult problem. In this paper, we construct a power line data set using UAV images and classify the data according to the image clutter (IC). A method combining line detection and semantic segmentation is used. This method is divided into three steps: First, a multi-scale LSD is used to determine power line candidate regions. Then, based on the object-based Markov random field (OMRF), a weighted region adjacency graph (WRAG) is constructed using the distance and angle information of line segments to capture the complex interaction between objects, which is introduced into the Gibbs joint distribution of the label field. Meanwhile, the Gaussian mixture model is utilized to form the likelihood function by taking the spectral and texture features. Finally, a Kalman filter (KF) and the least-squares method are used to realize power line pixel tracking and fitting. Experiments are carried out on test images in the data set. Compared with common power line extraction methods, the proposed algorithm shows better performance on images with different IC. This study can provide help and guidance for power line inspection.

The point segmentation of power lines and towers aims to use unmanned aerial vehicles (UAVs) for the inspection of power facilities, risk detection and modelling. Because of the unclear spatial relationship between the point clouds, the point segmentation of power lines and towers is challenging. In this paper, the power line and tower point datasets are constructed using Light Detection and Ranging (LiDAR) and a point segmentation method is proposed based on multiscale density features and a point-based deep learning network. First, the data are blocked and the neighbourhood is constructed. Second, the point clouds are downsampled to produce sparse point clouds. The point clouds before and after sampling are rotated, and their density is calculated. Next, a direct mapping method is selected to fuse the density information; a lightweight network is built to learn the features. Finally, the point clouds are segmented by concatenating the local features provided by PointCNN. The algorithm performs effectively on different types of power lines and towers. The mean interaction over union is 82.73%, and the overall accuracy can reach 91.76%. This approach can achieve the end-to-end integration of segmentation and provide theoretical support for the segmentation of large scenic point clouds.

The prediction of geothermal high-temperature anomalies along the plateau railway will be helpful in the construction of the project and its later management. Taking the Sichuan–Tibet railway as the study area and based on Landsat8 thermal infrared images, map data, and measured data regarding the cause and distribution of geothermal high-temperature anomalies, through correlation analysis, we selected six impact factors including the LST, combined entropy of geological formation, fault density, buffer distance to rivers, magnetic anomaly, and earthquake peak acceleration as the input maps of the model. The index-overlay information model, the weights of the entropy information model, and the weights of the evidence information model were established to quantitatively predict the geothermal anomaly in the study area, and the prediction maps were divided into four classes. The results show that the weights of the evidence information model achieved a high prediction accuracy; the success index and the ratio of the high anomaly area reached 0.0053% and 0.872, respectively, and the spatial distribution of the geothermal points is basically consistent with the prediction results. This research can act as a reference for the design and construction of the Sichuan–Tibet railway.

The water vapor budget (WVB) over the Tibetan Plateau (TP) is closely related to the large-scale atmospheric moisture transportation of the surrounding mainland and oceans, especially for the Indo-Pacific warm pool (IPWP). However, the procession linkage between the WVBs over the TP and its inner basins and IPWP has not been sufficiently elucidated. In this study, the relationship between the summer WVB over the TP and the IPWP was quantitatively investigated using reanalysis datasets and satellite-observed sea surface temperature (SST). The results show that: (1) the mean total summer vapor budget (WVB_t) over the TP in the period of 1979–2018 was 72.5 × 10⁶ kg s⁻¹. Additionally, for the 13 basins within the TP, the summer WVB has decreased from southeast to northwest; the Yarlung Zangbo River Basin had the highest WVB (33.7%), followed by the Upper Yangtze River Basin, Ganges River Basin and Qiangtang Plateau. (2) For the past several decades, the WVB_t over the TP has experienced an increasing trend (3.81 × 10⁶ kg s⁻¹ decade⁻¹), although the southern boundary budget (WVB_s) contributed the most and is most closely related with the WVB_t, while the eastern boundary budget (WVB_e) experienced a decreasing trend (4.21 × 10⁶ kg s⁻¹ decade⁻¹) which was almost equal to the interdecadal variations of the WVB_t. (3) For the IPWP, we defined a new warm pool index of surface latent heat flux (WPI-slhf), and found that an increasing WPI-slhf would cause an anticyclone anomaly in the equatorial western Indian Ocean (near 70° E), resulting in the increased advent of water vapor to the TP. (4) On the interdecadal scale, the correlation coefficients of the variation of the summer WVB_t over the TP with the WPI-slhf and Indian Ocean Dipole (IOD) signal were 0.86 and 0.85, respectively (significant at the 0.05% level). Therefore, the warming and the increasing slhf of the IPWP would significantly contribute to the increasing WVB of the TP in recent decades.

As one of the most significant physical processes in the ocean, mesoscale eddies play an import role in the local distributions of temperature, salinity, ocean current field and ecosystem through vertical mixing and horizontal advection. In addition, mass transport is importantly affected by the propagation of mesoscale eddies. Researches on the characteristics of mesoscale eddies and their effects on chlorophyll-a concentration in the Indo Pacific Warm Pool (20°S~20°N，60°E~170°W), the key area influencing the global climate change, help to further understand the bio-physical coupling processes in this domain. Using the remote sensing data from 1998 to 2018, combined with singular value decomposition, correlation analysis and other statistical methods, we have studied the distribution characteristics of mesoscale eddies with lifetime exceeding 4 weeks and the correlation with chlorophyll-a concentration in the Indo Pacific Warm Pool. The short-lived mesoscale eddies account for more than 70.0% and most of mesoscale eddies are nonlinear and propagate west. The seasonal numbers of mesoscale eddies vary insignificantly in the whole domain and plenty of the eddies generate in sea domains of 5°S~20°S and 5°N~20°N. The number of mesoscale eddies has little effect on chlorophyll-a concentration and the correlation between the kinetic energy of mesoscale eddies and chlorophyll-a concentration show both positive and negative.

The Tibetan Plateau (TP), atmosphere, and Indo-Pacific warm pool (IPWP) together constitute a regional land–atmosphere–ocean water vapor transport system. This study uses remote sensing data, reanalysis data, and observational data to explore the spatiotemporal variations of the summer atmospheric water cycle over the TP and its possible response to the air-sea interaction in the IPWP during the period 1958–2019. The results reveal that the atmospheric water cycle process over the TP presented an interannual and interdecadal strengthening trend. The climatic precipitation recycle ratio (PRR) over the TP was 18%, and the stronger the evapotranspiration, the higher the PRR. On the interdecadal scale, the change in evapotranspiration has a significant negative correlation with the Pacific Decadal Oscillation (PDO) index. The variability of the water vapor transport (WVT) over the TP was controlled by the dynamic and thermal conditions inside the plateau and the external air-sea interaction processes of the IPWP. When the summer monsoon over the TP was strong, there was an anomalous cyclonic WVT, which increased the water vapor budget (WVB) over the TP. The central and eastern tropical Pacific, the maritime continent and the western Indian Ocean together constituted the triple Sea Surface Temperature (SST) anomaly, which enhanced the convective activity over the IPWP and induced a significant easterly wind anomaly in the middle and lower troposphere, and then generated pronounced easterly WVT anomalies from the tropical Pacific to the maritime continent and the Bay of Bengal. Affected by the air-sea changes in the IPWP, the combined effects of the upstream strengthening and the downstream weakening in the water vapor transport process, directly and indirectly, increased the water vapor transport and budget of TP.