Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

Traditional villages are the spatial carriers of the excellent traditional culture of the Chinese nation, the concentrated expression of thousands of years of farming civilization, and a nonrenewable precious cultural heritage. However, in recent decades, with the rapid development of urbanization and modernization, traditional villages have rapidly declined or even disappeared. Therefore, exploring the spatial distribution characteristics and influencing factors of traditional villages and clarifying their formation and development laws, can provide a new perspective for the continuity protection of traditional villages on a cross-regional scale. This study takes six batches of 462 traditional villages in the Wuling Mountains, Hunan, China as the research object. On the basis of the analysis of the spatial distribution characteristics of overall and different ethnic minority traditional villages, this research proposes the idea of simulating the spatial distribution mechanism of traditional villages by using the multiscale geographical weighted regression (MGWR) model. Results indicate that (1) the spatial distribution of the traditional villages of the overall and different ethnic minorities shows significant agglomeration characteristics (among them, traditional Miao and Tujia villages have formed the main and sub cores of the space gathering of traditional villages in the Wuling Mountains in Hunan); and (2) compared with the least squares (OLS) method and the classic geographical (GWR) weighted regression model, the MGWR model can reflect the impact scales of different factors, and the simulation effect is better. From the perspective of influence factors, natural factors are the basis of affecting the distribution of traditional villages in the Wuling Mountain area and are generally positively correlated. Socioeconomic factors have a dual influence on the spatial distribution of traditional villages and generally have a negative correlation. Cultural factors are the key to the spatial distribution of traditional villages and are generally positively correlated. Spatial factors have no significant impact on the spatial distribution of traditional villages. On the basis of these conclusions, this study discusses the relevant strategies for the protection and development of traditional villages.

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

The goaf left behind after mining has the potential to induce serious geological disasters due to the damaged internal structure of the rock. Estimating the boundary of the underground goaf can effectively control the occurrence of such disasters. However, traditional geophysical methods are inefficient and expensive and are particularly difficult to apply for a wide detection range. This paper proposes a new method for estimating the boundary of underground goaf using the differential interference synthetic aperture radar technique (DInSAR). More specifically, DInSAR is used to obtain the isoline of the subsidence basin above the goaf, and the direction of the two main sections of the goaf is then determined according to the basic law of mining subsidence. Following this, the basic principles of the probability integral and the graphical methods are combined to determine the mining boundary of the strike section and the incline section of the goaf. Finally, six geometric parameters reflecting the boundary of the goaf are obtained. Experiments on simulated and measured data indicate that the proposed method is feasible, with the average relative errors of the simulated and measured data reaching and maintained at 2.2% and 3.7%, respectively.