摘要
在大风等极端天气下,铁路沿线的易漂浮物(如塑料大棚等)可能被吹起,从而引发故障并威胁铁路运行安全;早期的易漂浮物侵限检测主要依赖人工巡检,效率低且存在漏检,难以实现广域的实时监测。本文基于GEE云平台,协同Sentinel-1雷达影像和Sentinel-2光学影像,构建多维特征空间,利用随机森林算法实现2019~2023年成昆铁路西昌段沿线大棚识别;通过结合大棚核密度、距铁路距离及风速、风向数据,构建大棚侵限风险评估模型,量化铁路沿线各区域可能引发大棚侵限风险的概率等级。结果显示:①识别的大棚区域的五年平均精确率为93.9%,召回率为94.95%。②大棚主要分布在研究区西北部和西南部。近五年间大棚数量快速增加,2021~2022年增幅最大,年增量达20.27 km^(2)。③春季和冬季的高风险区域广泛分布于研究区西北部和西南部;夏季和秋季的高风险区较少,主要集中在研究区东北部的少数区域。研究成果对提升铁路运营的安全性和稳定性具有重要意义。
Railways constitute an integral part of contemporary transportation infrastructures,with their safety and seamless operation being intrinsically linked to economic stability and growth.In recent years,the expansion of agricultural activities has led to a notable rise in the presence of lightweight objects,such as agricultural films and plastic greenhouse covers,along railway corridors.Under extreme weather conditions,particularly strong winds,these objects may be lifted and encroach upon the railway’s safety perimeter-an event termed“encroachment”.Such encroachment can obscure overhead power lines(catenaries),causing power outages and posing significant risks to railway safety.Historically,the early detection of these encroachment relied heavily on manual inspections,a method that proved inefficient,susceptible to oversight,and ill-equipped to satisfy the demands for extensive,real-time surveillance.Consequently,there is an exigent need for accurate,rapid identification of floating debris along railways and comprehensive risk assessments of large-scale intrusion potential.This research harnesses the capabilities of the Google Earth Engine(GEE)cloud platform,amalgamating Sentinel-1 synthetic aperture radar(SAR)data with Sentinel-2 optical imagery to construct a multidimensional feature space comprising 17 variables.These encompass dual radar backscatter coefficients(VV and VH),ten spectral reflectance bands(B2 through B8A and B11,B12),and five spectral vegetation indices(NDVI,SAVI,MNDWI,NDBI,EBSI).Employing the random forest(RF)algorithm,the study successfully delineated greenhouse regions along the Xichang segment of the Chengdu-Kunming railway spanning from 2019 to 2023.Furthermore,by incorporating datasets on greenhouse density kernels,proximity to rail tracks,and local wind speeds/directions,a sophisticated risk assessment model was formulated to quantify the likelihood of intrusion events across different zones adjacent to the railway.Key findings include:(1)High accuracy in greenhouse detection.Utilizi
作者
杨雅洁
尹高飞
汤承玉
陈瑞
谢江流
马杜娟
刘建涛
冯权泷
YANG Yajie;YIN Gaofei;TANG Chengyu;CHEN Rui;XIE Jiangliu;MA Dujuan;LIU Jiantao;FENG Quanlong(Faculty of Geosciences and Engineering,Southwest Jiaotong University,Chengdu 611756,China;School of Surveying and Geo-Informatics,Shandong Jianzhu University,Jinan 250101,China;College of Land Science and Technology,China Agricultural University,Beijing 100193,China)
出处
《时空信息学报》
2024年第5期573-584,共12页
JOURNAL OF SPATIO-TEMPORAL INFORMATION
基金
科技基础资源调查专项项目(2022FY1002042)。
关键词
铁路
易漂浮物
大棚
侵限
Sentinel影像
随机森林
GEE
风险评估
railway
floating objects
greenhouses
encroachment
Sentinel images
random forest algorithm
Google Earth Engine
risk assessment
