Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

Impact of human settlements quality on urban vitality based on multisource data:A case study of Shahekou District,Dalian,China

Urban geography has always been concerned about the influence of human settlements on urban vitality,but few studies reveal the influence of human settlements on urban vitality at a micro-scale.This paper analyzes the spatial distribution characteristics of human settlements’quality and urban vitality at the micro-scale using Geodetectors and geographic weighted regression to analyze the relationship between human settlements and urban vitality.The results are shown as follows:there is still a significant development space for human settlements quality in Shahekou District,with obvious spatial dependence characteristics and significant gaps between various systems;the urban vitality of Shahekou District has obvious timeliness,and the urban vitality undergoes significant changes over time,which is related to the human settlements quality.The spatial distribution presents a single core spatial distribution structure with strong relative stability.The spatial distribution of cold and hot spots shows a pattern of“high in the north and low in the south,high in the east and low in the west”,with an increasing trend from southwest to northeast;the reachability of public transport has a significant impact on urban vitality.Its synergy with other variables is the leading force forming the spatial distribution of urban vitality.The environmental system,support system and social system are the significant factors affecting the urban vitality of Shahekou District.

Construction and implementation of multisource spatial data management system of China's coastal zone and offshore

To meet the increasing demand of national spatial database infrastructure construction and application, a concept model of China's coastal zone scientific data platform is established based on the information feature analysis of a compound dataset, consisting of remote sensing data and conventional data. Based on this concept model, the detailed logical database structure and the storage strategy of remote sensing data and their metadata using ArcSDE are designed. The complicated technology of multisources data combination in this research is crucial to the future coastal zone and offshore database construction and practical running, which will provide intelligent information analysis and technological service for coastal zone and offshore investigation, research, development and management.

Integrating multisource RS data and GIS techniques to assist the evaluation of resource-environment carrying capacity in karst mountainous area

The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remote sensing(RS)and geographic information system(GIS)provides data sources and processing platform for RECC monitoring.This study analyzed and established the evaluation index system of RECC by considering particularity in the karst mountainous area of Southwest China;processed multisource RS data(Sentinel-2,Aster-DEM and Landsat-8)to extract the spatial distributions of nine key indexes by GIS techniques(information classification,overlay analysis and raster calculation);proposed the methods of index integration and fuzzy comprehensive evaluation of the RECC by GIS;and took a typical area,Guangnan County in Yunnan Province of China,as an experimental area to explore the effectiveness of the indexes and methods.The results showed that:(1)The important indexes affecting the RECC of karst mountainous area are water resources,tourism resources,position resources,geographical environment and soil erosion environment.(2)Data on cultivated land,construction land,minerals,transportation,water conservancy,ecosystem services,topography,soil erosion and rocky desertification can be obtained from RS data.GIS techniques integrate the information into the RECC results.The data extraction and processing methods are feasible on evaluating RECC.(3)The RECC of Guangnan County was in the mid-carrying level in 2018.The midcarrying and low-carrying levels were the main types,accounting for more than 80.00%of the total study area.The areas with high carrying capacity were mainly distributed in the northern regions of the northwest-southeast line of the county,and other areas have a low carrying capacity comparatively.The coordination between regional resource-environment status and socioeconomic development is the key to improve RECC.This study explores the evaluation index system of RECC in karst mountainous area and the application of multisource RS data and GIS techniques in the comprehensive evaluation.The methods can be applied in related fields to provide suggestions for data/information extraction and integration,and sustainable development.

RANDOM SETS: A UNIFIED FRAMEWORK FOR MULTISOURCE INFORMATION FUSION

The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and so on. However,the information is modeled and fused traditionally in particular,name some of the known theories: evidential,fuzzy sets,possibilistic,rough sets or conditional events,etc. For several years,researchers have explored the unification of theories enabling the fusion of multisource information and have finally considered random set theory as a powerful mathematical tool. This paper attempts to overall review the close relationships between random set theory and other theories,and introduce recent research results which present how different types of information can be dealt with in this unified framework. Finally,some possible future directions are discussed.

A Multisource Contour Matching Method Considering the Similarity of Geometric Features

The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.

Comprehensive drought monitoring in Yunnan Province, China using multisource remote sensing data

Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index(TRCI), vegetation condition index(VCI), temperature condition index(TCI), and elevation factors. Principal component analysis(PCA) and analytic hierarchy process(AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that:(1) The comprehensive drought index(CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS(China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring.(2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four droughtprone centres.(3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period(61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend(slope > 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province.

Effects of periodically varying codes on separation of multisource blended data

Current exploration needs are satisfied by multisource technology,which offers low cost,high efficiency,and high precision.The delay time,which determines the separation effects of the multisource blended data,is one of the most crucial parameters in the acquisition and separation of multisource data.This study uses the deblending method of multisource data based on a periodically varying cosine code and analyses the effects of the two parameters,namely,the period amplitude and period length,used in this method on the separation of the multisource blended data.Meanwhile,the obtained coherence data is used to prove the correlation between the separation of multisource data and the two parameters.Examples of synthetic and field data are adopted to demonstrate that from a qualitative perspective,increasing the amplitude of the periodic code improves the separation effect within a reasonable delay time range.When the period length varies in a suitable range,the secondary noise becomes relatively incoherent,resulting in the separation result with a higher signal-to-noise ratio(SNR).From a quantitative perspective,the significant values(Sig.)of the period amplitude and length on the SNRs are less than 0.05,verifying the correlation between the separation of multisource data and the two parameters.

Hopf bifurcation of nonlinear system with multisource stochastic factors

The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter.Firstly,the nonlinear system with multisource stochastic fac-tors is reduced to an equivalent deterministic nonlinear system by the sequential orthogonal decomposi-tion method and the Karhunen-Loeve(K-L)decomposition theory.Secondly,the critical conditions about the Hopf bifurcation of the equivalent deterministic system are obtained.At the same time the influence of multisource stochastic factors on the Hopf bifurcation for the proposed system is explored.Finally,the theorical results are verified by the numerical simulations.

基于多源异构数据的智能辅助选线应用研究

长久以来电力选线方案优劣严重依赖于人工经验、各专业配合度、收资准确性及完整性,本文结合实际输电线路工程,基于多源异构数据利用多种群遗传算法开展架空输电线路智能辅助选线工作。实验结果表明,智能辅助选线推荐方案能够很好兼顾线路经济性和合理性,能够为设计人员提供较高实用价值的路径初选方案。

机载SAR、热红外与高光谱联合的溢油检测实验方案设计

快速并准确进行溢油检测对海洋生态环境具有重要意义,遥感技术因其“站得高、看得远、观得全”的独特优势成为海上溢油检测的重要手段。为综合利用各种遥感监测手段的优势准确提取海面油膜,于2022年10月22日至23日开展外场模拟溢油实验,利用无人机同步获取机载SAR、热红外与高光谱数据。使用支持向量机(SVM)和随机森林(RF)模型进行溢油检测,研究并分析以SAR数据为基础加入高光谱与热红外数据进行溢油检测的优势。结果表明,RF模型检测Dataset#1的F1-Score达到72.74%,错分现象明显改善。SVM和RF模型对Dataset#2的F1-Score分别达到99.52%和99.64%。利用多维度遥感数据进行溢油检测,为多源遥感手段在海洋溢油检测的应用提供了技术支撑。

京津冀平原浅层地下水漏斗演变规律与影响因素

为研究华北地区河湖生态补水对地下水漏斗演变的影响,以京津冀平原浅层地下水漏斗2003年至2022年的相对变化作为识别目标,从气象因素、地形因素、人为因素和含水层水力学特性4个方面进行考虑,选取8个具体指标构建特征变量数据集,使用逻辑回归(logistic regression, LR)、支持向量机(support vector machine, SVM)和随机森林(random forest, RF)方法建立漏斗演变识别模型,并利用敏感度、特异度和决定系数R^(2)对拟合效果进行对比评价,结果显示随机森林为最优模型。进而利用模型分析研究区地下水漏斗演变规律,阐明具体因素对漏斗演变的影响作用。研究表明:京津冀平原区浅层地下水漏斗在2010年之前整体呈扩张趋势,之后在部分地区呈现缩减和消失的态势。河湖补水前,地下水漏斗发展主要受开采影响,其重要度约50%;2018年后河湖补水对抑制漏斗扩张发挥了较为明显的作用,重要度达16%。从发展过程来看,地下水开采依然是控制京津冀平原浅层地下水漏斗变化最重要的因素。对比宁柏隆和高蠡清两个典型浅层地下水漏斗的发展变化可知,河道生态补水对宁柏隆漏斗变化的贡献率接近10%,而对高蠡清漏斗变化影响的重要度仅为1%,因此持续的河流生态补水对宁柏隆漏斗水位恢复会产生积极影响,而对于高蠡清漏斗则需要以水源置换压减农业灌溉地下水量为关键手段实现水位恢复。

GIS绝缘子金属污秽缺陷不同程度间歇性放电诊断分析研究

针对现场气体绝缘组合电器(GIS)绝缘缺陷局部放电漏报、误报情况频发的问题,利用真型GIS搭建局放多源检测试验平台,开展绝缘子表面金属污秽缺陷间歇性放电试验,采用不同局放检测法研究不同程度间歇性放电特性变化规律。研究结果表明:金属污秽缺陷放电量随着放电时间的延长呈现先升后降的变化趋势;放电时间间隔整体呈现增加的变化趋势;超声检测法和化学检测法无法实现金属污秽间歇性放电的有效感知;由4组50个工频周期特高频(UHF)相位分辨局部放电(PRPD)图谱数据所构成的200个工频周期UHF PRPD图谱中能初步得出间歇性放电分布规律;处于间歇性放电状态的金属污秽缺陷不会逐渐发展演变成击穿放电故障。研究成果对拓展间歇性放电特征的认知及现场对GIS放电缺陷的检测具有重要意义。

基于邻域显著性的可见光和SAR遥感图像海面舰船协同检测方法

在遥感图像舰船检测任务中,可见光图像细节和纹理信息丰富,但成像质量易受云雾干扰,合成孔径雷达(SAR)图像具有全天时和全天候的特点,但图像质量易受复杂海杂波影响。结合可见光和SAR图像优势的协同检测方法可以提高舰船目标的检测性能。针对在前后时相图像中,舰船目标在极小邻域范围内发生轻微偏移的场景,该文提出一种基于邻域显著性的可见光和SAR多源异质遥感图像舰船协同检测方法。首先,通过可见光和SAR的协同海陆分割降低陆地区域的干扰,并通过RetinaNet和YOLOv5s分别进行可见光和SAR图像的单源目标初步检测;其次,提出了基于单源检测结果对遥感图像邻域开窗进行邻域显著性目标二次检测的多源协同舰船目标检测策略,实现可见光和SAR异质图像的优势互补,减少舰船目标漏检、虚警以提升检测性能。在2022年烟台地区拍摄的可见光和SAR遥感图像数据上,该方法的检测精度AP50相比现有舰船检测方法提升了1.9%以上,验证了所提方法的有效性和先进性。

基于多源数据的青海东部城市群气候承载力评估与时空格局分析

为探索高原地区城市化与气候变化之间的相互作用机制。从气候承载力基本内涵出发,基于气候条件与人类活动相互作用关系,同时考虑气候资源和极端气候事件双重属性,本文识别并构建了包括气候天然容量、气候极端事件压力、城市气候压力以及城市协调发展能力四方面要素的气候承载力评估指标体系,以青海东部城市群为例,应用模型探讨了气候承载力的时空分布格局及变化规律。结果显示:从时间分布上来看,2001—2020年青海东部城市群气候承载力整体呈下降趋势,且因受到气候天然容量和极端气候事件压力的影响,波动幅度较大;从空间分布上来看,青海东部城市群低和较低气候承载力区主要集中于民和—乐都—西宁—湟中沿线大片区域,中气候承载力区零散分布于湟水谷地两岸区域,高承载力和较高承载力区则主要分布于该地区的西北部和东部北地区。研究结果提示本文建立的气候承载力评估指标体系对于青海东部城市群是合理可行的,可以描述城市群城市气候承载力在时空上的格局演变过程,可作为气候变化风险预警的依据。

多源融合降水实况分析产品在海河流域的适用性评估

基于逐小时地面站点降水观测数据和国家气象信息中心研发的5 km多源融合降水实况分析产品,采用算术平均法对2021年6~9月海河流域面雨量进行估算。通过相关系数、平均误差和均方根误差等多种评估指标,客观定量评估多源融合降水实况分析产品在海河流域的适用性。结果表明:多源融合降水实况分析产品与地面观测资料估算的面雨量结果基本一致,能较好地反映2021年6~9月海河流域面雨量的时空变化特征,但在量值上存在高估且随着降水量增加,估算误差也越大。分区对比,融合实况分析产品和站点的误差与子流域的平均降水量、海拔高度和面积密切相关。对于各量级面雨量出现的频次,融合实况分析产品与站点整体相差不大,准确率可以达到90%及以上。总体而言,5 km多源融合降水实况分析产品的质量较高,可进一步应用于海河流域精细化面雨量监测业务中。

船舶交通智能感知融合与辅助决策方法综述

船舶交通智能感知与辅助决策是船舶智能交通的核心内容,是保障未来船舶智能航行安全的重大需求。在船舶智能交通系统中,航行信息感知与融合理解发挥着关键作用,通过各种传感技术和信息传输、处理技术,实时获取、分析船舶与环境等相关信息,直接影响着船舶智能航行的态势判断和风险评估能力,同时基于数据融合分析的辅助决策生成效果决定了船舶自主航行能力,关系着船舶交通的安全和效率。船舶智能交通下的感知融合具有多级体系特征,研究者在通信传输、信息交互和数据融合3个层级进行了多方面改进。深入探讨目前船舶智能交通感知与融合理解的发展和面临的挑战,针对船舶智能航行过程中对环境目标的感知融合具有复杂动态的特点,研究网络通信保障、感知信息交互、智能化融合分析3个层级间的相互关联关系,分析面向复杂水域的船舶智能航行感知体系,讨论船舶对航行环境的识别和认知能力提升的潜在空间和应用前景,有助于为船舶航行的自主化、智能化提供技术支持,服务于船舶交通智能化整体水平的提升。

数字地下空间信息检测管理平台研究

地下检测是诊断和预防地下病害的重要手段,但由于传统的城市地下检测数据平台架构限制,各组织机构独立管理和上传数据,导致数据共享难以实现。针对这一问题,利用先进的互联网、物联网技术,通过感、传、知、用四层架构建立数据综合信息管理平台,加强检测数据的汇聚融合、共享开放和开发利用,发挥数据的基础资源作用和分析引擎作用,提高政府决策科学化水平和管理效率,对于保障城市安全和预防道路风险隐患具有重要意义。数字地下空间信息检测管理平台可实现多源数据的统一管理,促进数据有序流动,对检测结果进行直观的展示、查询和工程化管理。文中利用地图数据实现地下空间数据与检测数据的匹配,实现检测结果的可视化表达。通过对数据的统计分析和评价来精准定位安全隐患及事故,促进城市建设协调机制更加健全;同时检测数据可通过安卓端以图片、文字、视频等多种方式实时地传送到信息管理平台,实现多端数据推送与接收,推动数据资源的开发利用,提高服务效率。该信息管理平台可实现与数据道路、智慧城市的完美对接,通过对其在不同场景的示范应用,不断完善平台功能、性能,验证了该信息管理平台的服务能力,提高了地下检测数据信息化的整体水平。

面向磁浮轨道的多源点云数据的混合滤波方法

在磁浮轨道的仿真数据处理过程中,磁浮轨道点云数据的滤波提取是重要环节之一,实际应用应根据待提取的磁浮数据特性,采用高效的滤波方法。磁浮轨道的点云数据对象主要包括由无人机(UAV)倾斜摄影获取的磁浮轨道的图像数据并经过三维重建后形成的稠密点云数据、由手持式激光雷达扫描磁浮轨道获取的激光点云数据。根据这两种点云的数据特性,考虑磁浮轨道四周复杂场景的点云环境,分别对两种点云进行混合滤波。首先,对激光点云数据采用八叉树下采样方法,有效降低了点云数据的数量级,节省了运行时间。然后,分别对激光点云与稠密点云数据采用布料模拟滤波(CSF)方法,过滤了地平面点云数据,保留了非地面点云数据;采用统计离群点去除(SOR)滤波方法,筛除了大量离群点;根据磁浮轨道特征,采用直通滤波过滤了坐标范围外的点云数据。实验结果表明,在不影响磁浮轨道结构的前提下,对于采用八叉树下采样方法的激光点云数据和没有采用八叉树下采样的稠密点云数据,该方法的滤波率分别为86.15%和64.76%,经混合滤波后的两种点云数据的结构近似,点云数量处于同一数量级,为磁浮轨道点云特征提取等后续任务提供了有效保障。

2020年河南省常住人口密度数据集

精细的人口空间数据对于城市资源配置和规划有着极其重要的作用。针对目前公开的人口数据集难以满足区域精细化研究需求的问题,本研究选取夜间灯光数据、POI数据、地表覆盖数据和地形数据建立人口密度空间化模型特征库,使用基于贝叶斯优化的LightGBM模型(BO-LightGBM)对河南省2020年乡镇(街道)人口进行建模,反演河南省200米分辨率的人口密度空间数据。利用第七次人口普查数据进行验证,决定系数R^(2)为0.934,平均绝对误差为0.134,均方误差为0.034,相比于WorldPop数据集有较大的提升,证明本研究的人口空间化结果是可行且准确的,能够较好地反映河南省2020年的人口密度状况,可用于更高精度的人口空间分析。