A point cloud segmentation method for power lines and towersbased on a combination of multiscale density features andpoint-based deep learning

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.

Dynamic analysis of the Wenchuan Earthquake disaster and reconstruction with 3-year remote sensing data

Earth observation is an effective technique that plays an important role in earthquake damage reduction and reconstruction.This paper introduces the results of dynamic analysis on monitoring and assessing heavily impacted areas affected by the Wenchuan Earthquake using remote sensing data acquired in the past 3 years from 2008 to 2010.Immediately after the disaster on 12 May 2008,the Chinese Academy of Sciences launched a project entitled‘Wenchuan Earthquake Disasters Monitoring and Assessment Using Remote Sensing Technology.’More than 400 images from 17 satellites and 20.2TB airborne remote sensing data were acquired to facilitate quick monitoring and evaluation of severely damaged areas in 14 counties.Results of the image analyses were forwarded on a timely basis to assist with consultative service and decisionmaking support.In subsequent years,in order to monitor the process of environmental restoration and reconstruction,airborne optical remote sensing images covering most of the severely damaged areas were again acquired in May 2009 and April 2010.These images were analyzed and compared along with images from 2008.Results were useful in support of further work on environmental protection and reconstruction in earthquake-damaged areas.Three typical areas were selected for illustrative purposes including Tangjiashan Barrier Lake,Beichuan County,and counties of Yingxiu and the new Beichuan.These results well demonstrate the importance and effectiveness of the utility of earth observation for disaster mitigation and reconstruction.