Landscape characters in estuarine regions generally controlled by tidal regimes and human activities like road construction.In this work,tidal channels and road construction in the Yellow River Delta(YRD)were extracte...Landscape characters in estuarine regions generally controlled by tidal regimes and human activities like road construction.In this work,tidal channels and road construction in the Yellow River Delta(YRD)were extracted by visual interpretation methods so as to decipher impacts of tidal channel development and road construction on landscape patch change during 1989–2016.Spatial distribution history of three wetlands,which covered by Phragmites australis(freshwater marsh,FM),Suaeda salsa(salt marsh,SM),and mudflats(MD)were also established.Results indicated that tidal channel,number,frequency,and fractal dimension were all the maximum in 2003,and the minimum in 1998,respectively.Road length,number,and density showed increasing trend during 1989–2016.MD were the predominant landscape type,followed by FM and SM during 1989-2016.Principal component analysis implied two extracted factors,F1 and F2,which could represent 91.93% of the total variations.F1 mainly proxied tidal channel development,while F2 represented road construction.A multiple linear regression analysis showed positive effects of both F1 and F2 on FM patch numbers and negative impacts on SM patch areaes with R^2 values of 0.416 and 0.599,respectively.Tidal channels were negatively related to MD patch numbers,while roads were positively related to that.In any case,road construction showed larger impacts on landscape type shifting than that of tidal channel development in the YRD.展开更多
为了解决遥感道路提取中边缘细节特征利用不充分,以及复杂背景遮挡区域的道路难以实现准确分割等问题,提出一种基于边缘引导和多尺度感知的遥感道路提取模型(Edge-guidance and Multi-scale perception U-Net,EMUNet)。以U-Net为基础,...为了解决遥感道路提取中边缘细节特征利用不充分,以及复杂背景遮挡区域的道路难以实现准确分割等问题,提出一种基于边缘引导和多尺度感知的遥感道路提取模型(Edge-guidance and Multi-scale perception U-Net,EMUNet)。以U-Net为基础,增加遥感图像的Canny边缘检测结果作为输入,并通过设计结合注意力的边缘引导融合模块对各层编码器进行特征引导,以此充分利用边缘信息提高最终的道路提取质量;其次,针对图像中存在的背景遮挡问题,通过构建多尺度并行空洞卷积模块增强网络的多尺度感知能力,从而捕获更多的上下文信息,对一些受到背景遮挡的区域实现准确提取。在Massachusetts道路数据集上进行实验验证,与U-Net相比,EMUNet能实现对细小道路和受遮挡区域更准确的分割,并且召回率、F1分数和交并比均优于其他对比算法,能够实现更为完整和准确的道路信息提取。展开更多
随着信息技术的快速发展,建筑信息模型(Building Information Modeling,BIM)在建筑行业中的应用日益广泛。本论文针对路桥项目的特殊性,以解决实际问题为目标,探讨了BIM在路桥项目管理与协调中的应用[1]。通过对实际项目的案例研究,提...随着信息技术的快速发展,建筑信息模型(Building Information Modeling,BIM)在建筑行业中的应用日益广泛。本论文针对路桥项目的特殊性,以解决实际问题为目标,探讨了BIM在路桥项目管理与协调中的应用[1]。通过对实际项目的案例研究,提出了一套基于BIM的路桥项目管理与协调的方法和工具,以优化项目的生命周期管理和提高项目的整体效率。本研究为路桥项目的实际应用提供了有益的参考和指导。展开更多
基金Under the auspices of National Key Research and Development Project(No.2017YFC0505901)
文摘Landscape characters in estuarine regions generally controlled by tidal regimes and human activities like road construction.In this work,tidal channels and road construction in the Yellow River Delta(YRD)were extracted by visual interpretation methods so as to decipher impacts of tidal channel development and road construction on landscape patch change during 1989–2016.Spatial distribution history of three wetlands,which covered by Phragmites australis(freshwater marsh,FM),Suaeda salsa(salt marsh,SM),and mudflats(MD)were also established.Results indicated that tidal channel,number,frequency,and fractal dimension were all the maximum in 2003,and the minimum in 1998,respectively.Road length,number,and density showed increasing trend during 1989–2016.MD were the predominant landscape type,followed by FM and SM during 1989-2016.Principal component analysis implied two extracted factors,F1 and F2,which could represent 91.93% of the total variations.F1 mainly proxied tidal channel development,while F2 represented road construction.A multiple linear regression analysis showed positive effects of both F1 and F2 on FM patch numbers and negative impacts on SM patch areaes with R^2 values of 0.416 and 0.599,respectively.Tidal channels were negatively related to MD patch numbers,while roads were positively related to that.In any case,road construction showed larger impacts on landscape type shifting than that of tidal channel development in the YRD.
文摘为了解决遥感道路提取中边缘细节特征利用不充分,以及复杂背景遮挡区域的道路难以实现准确分割等问题,提出一种基于边缘引导和多尺度感知的遥感道路提取模型(Edge-guidance and Multi-scale perception U-Net,EMUNet)。以U-Net为基础,增加遥感图像的Canny边缘检测结果作为输入,并通过设计结合注意力的边缘引导融合模块对各层编码器进行特征引导,以此充分利用边缘信息提高最终的道路提取质量;其次,针对图像中存在的背景遮挡问题,通过构建多尺度并行空洞卷积模块增强网络的多尺度感知能力,从而捕获更多的上下文信息,对一些受到背景遮挡的区域实现准确提取。在Massachusetts道路数据集上进行实验验证,与U-Net相比,EMUNet能实现对细小道路和受遮挡区域更准确的分割,并且召回率、F1分数和交并比均优于其他对比算法,能够实现更为完整和准确的道路信息提取。
文摘随着信息技术的快速发展,建筑信息模型(Building Information Modeling,BIM)在建筑行业中的应用日益广泛。本论文针对路桥项目的特殊性,以解决实际问题为目标,探讨了BIM在路桥项目管理与协调中的应用[1]。通过对实际项目的案例研究,提出了一套基于BIM的路桥项目管理与协调的方法和工具,以优化项目的生命周期管理和提高项目的整体效率。本研究为路桥项目的实际应用提供了有益的参考和指导。