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.

As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development.

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 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.