室内楼层平面导航路网模型的自动生成     

李华蓉  彭映雪  李海明 《测绘通报》2021,(4):79-84,104

室内路网模型作为室内导航研究的基础,如何自动生成室内路网模型成为近年的研究热点。对于目前自动路网模型研究中出现的复杂环境适应度不够的问题,本文提出了一种室内楼层平面路网模型的自动提取方法。它将室内空间分为公共空间和专属空间两类,从而形成公共空间路径和专属空间路径,并用转换点将两类路径进行衔接;最终利用提取的公共空间路径、专属空间路径和连接路径构成楼层平面导航路网模型。基于此方法,将某学校教学楼一层建筑平面图生成平面自动路网模型,并从路径完整度、准确度及寻路情况3个方面与手动路网模型进行了对比分析,其表现良好。  相似文献   

车载LiDAR点云中的建筑物特征提取     

王婷婷 《北京测绘》2021,35(1):41-45

针对车载LiDAR点云数据处理复杂、时间长的问题,本文以地物不同特征值作为建筑物自动提取算法的依据,通过点云数据预处理、聚类分析等一系列流程最终实现一般建筑物点云的自动提取。通过两个实验区点云数据的提取与相应的实际地物进行精度分析对比,结果表明本文算法对实例测区环境下的不同建筑物点云提取具有较好的有效性,满足数字城市三维建模的精度要求。  相似文献   

面向野外地质调查工作的道路提取方法研究     

高婷  黄颖  郭明强 《测绘工程》2021,30(2):13-16

为弥补传统的依靠管理组织、法律、经济、教育等进行对野外地质工作安全管理工作的不足,通过百度SDK采集GPS步行轨迹数据,基于单向GPS轨迹数据的特点,进行轨迹的分割分段处理。最后利用BP神经网络算法拟合道路,通过反复实验确定BP网络的参数配置信息。并以中国地质大学西区为研究区域,提取路网信息,验证算法的有效性和实用性。为野外地质调查工作的信息化建设和救援指挥调度系统建设提供重要的技术支撑。  相似文献   

一种基于室内点云数据的建筑墙线提取     

孙宪龙  韩李涛  周丽娟  郭佳 《测绘工程》2021,30(2):65-71

针对室内点云数据无结构化属性、数据间无连接、不承载语义信息且数据点密度高的特点,结合建筑物点云几何特征和室内导航需求,通过数据降维简化建筑几何特征提取的复杂性,提出一种基于室内点云数据提取建筑物墙线的方法。该方法首先通过向特定方向投影,利用点云密度直方图完成天花板面、地板面和房间墙面的初步分割;然后将房间墙面点云数据向地面投影,生成点云分布矩阵并将其转化为二值图,利用Hough变换算法提取直线,并利用直线方程求取交点得到备选墙线;最后将备选墙线和墙线点云二值图进行叠加从而获取最终建筑墙线。  相似文献   

基于图形分割的面状符号自动填充算法     

寇媛  姚炎林  王军军 《测绘地理信息》2021,46(2):93-95

针对现有制图软件在面状符号填充方面存在的符号缺失、压盖等问题,提出了一种基于图形分割的面状符号自动填充算法。该算法通过计算面的骨架线,对复杂面进行分解,判断分解后的简单面的形状,分别赋予不同的制图表达规则,实现面状符号的自动填充。结果表明,该算法可以有效实现狭长以及包含狭长分支的不规则图形的合理填充,有效解决了填充符号与图形边界、注记的压盖问题,提高了制图自动化,大大减少了制图工作量。  相似文献   

Spatio‐temporal variability of the isotopic input signal in a partly forested catchment: Implications for hydrograph separation     

Carles Cayuela  Jrme Latron  Josie Geris  Pilar Llorens 《水文研究》2019,33(1):36-46

The isotopic composition of precipitation (D and ¹⁸O) has been widely used as an input signal in water tracer studies. Whereas much recent effort has been put into developing methodologies to improve our understanding and modelling of hydrological processes (e.g., transit‐time distributions or young water fractions), less attention has been paid to the spatio‐temporal variability of the isotopic composition of precipitation, used as input signal in these studies. Here, we investigated the uncertainty in isotope‐based hydrograph separation due to the spatio‐temporal variability of the isotopic composition of precipitation. The study was carried out in a Mediterranean headwater catchment (0.56 km²). Rainfall and throughfall samples were collected at three locations across this relatively small catchment, and stream water samples were collected at the outlet. Results showed that throughout an event, the spatial variability of the input signal had a higher impact on hydrograph separation results than its temporal variability. However, differences in isotope‐based hydrograph separation determined preevent water due to the spatio‐temporal variability were different between events and ranged between 1 and 14%. Based on catchment‐scale isoscapes, the most representative sampling location could also be identified. This study confirms that even in small headwater catchments, spatio‐temporal variability can be significant. Therefore, it is important to characterize this variability and identify the best sampling strategy to reduce the uncertainty in our understanding of catchment hydrological processes.  相似文献   

Landsat长时间序列的阳澄湖湖面围网时空变化     

计璐艳  尹丹艳  宫鹏 《遥感学报》2019,23(4):717-729

准确提取湖泊围网区域的时空分布信息对湖泊的保护和可持续发展具有重要意义。本文以阳澄湖为研究区域,收集该地区1984年—2017年所有的Landsat 5和Landsat 8影像(共计396景),提出了结合光谱和纹理特征的围网提取新算法,同时利用时间序列滤波消除年际间因数据不一致造成的偏差。以高清影像人工解译作为参考,阳澄湖围网提取结果的生产者精度在72.57%—88.53%,用户者精度在79.79%—98.10%,围网面积变化与文献记录吻合。结果表明,阳澄湖围网经历了"无围网期"(1984年—1994年)、"快速增长期"(1994年—1998年)、"巅峰期"(1999年—2002年)、"快速下降期"(2003年—2006年)和"稳定期"(2007年—2017年)5个阶段,最高达到100 km2,目前稳定在30 km2;通过研究围网区植被指数发现,2002年之后围网区浮水植物的种植面积增大;通过对比水质数据发现,2002年至今持续15年的围网拆除并未使阳澄湖恢复到80年代无围网养殖时期的II类水,其水质依然处于Ⅲ—Ⅳ类。因此在湖泊养殖开发过程中,政府应该坚持可持续发展道路,在不破坏湖泊水质的基础上发展湖泊经济。  相似文献   

River centerline extraction using the multiple direction integration algorithm for mixed and pure water pixels     

Quanwen Li  Xiaoxia Zhao  Yuming Wu 《地理信息系统科学与遥感》2019,56(2):256-281

The river centerline is a basic hydrological characteristic. Most prior studies have used remote sensing data to extract the river centerline from the open water region in a pure water pixel region. Extracting this type of river is relatively easy. However, extracting the centerline of a micro-river, which is mainly composed of mixed water pixels, is challenging. This paper presents a novel method, called the Multiple Direction Integration Algorithm (MDIA), to extract the river centerline using an image-enhancing method combined with river morphology. MDIA can be applied to regions mainly composed of pure water pixels, as well as to regions consisting of mixed water pixels in the index image. The method first calculates the normalized difference vegetation index (NDVI) and enhances the river linear structure using a Hessian matrix. Second, a small window is constructed as a circular structural element. In the window region, the local threshold is automatically obtained using water-oriented clustering segmentation and prior river knowledge to judge the pixel type. After completing the river centerline extraction in the current window, the next detecting window is generated to continue judgment. The structural element automatically executes river centerline judgment until the entire river centerline is extracted. The Landsat 8 images of six regions with different geomorphologies were chosen to analyze the method’s performance. The test sites include high mountain region, low mountain region, plains region with farmland and a residential region. The experimental results show that the optimal threshold of the processing results ranged from 0.2 to 0.3. In this range, the user’s accuracy is 0.813 to 0.997, and the producer’s accuracy is 0.981 to 1. The MDIA effectively and correctly extracts the river network in mixed-pixel regions. The presented method provides an effective algorithm for river centerline extraction that can be used to expand and update river datasets and provide reliable river centerline data for relevant hydrology studies.  相似文献   

A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways     

Suliman A. Gargoum  Karim El Basyouny 《地理信息系统科学与遥感》2019,56(6):864-893

Interest in using Light Detection and Ranging (LiDAR) technology in Transportation Engineering has grown over the past decade. The high accuracy of LiDAR datasets and the efficiency by which they can be collected has led many transportation agencies to consider mobile LiDAR as an alternative to conventional tools when surveying roadway infrastructure. Nonetheless, extracting semantic information from LiDAR datasets can be extremely challenging. Although extracting roadway features from LiDAR has been considered in previous research, the extraction of some features has received more attention than others. In fact, for some roadway design elements, attempts to extract those elements from LiDAR have been extremely scarce. To document the research that has been done in this area, this paper conducts a thorough review of existing studies while also highlighting areas where more research is required. Unlike previous research, this paper includes a thorough review of the previous attempts at data extraction from LiDAR while summarizing the detailed steps of the extraction procedure proposed in each study. Moreover, the paper also identifies common tools and techniques used to extract information from LiDAR for transportation applications. The paper also highlights common limitations in existing algorithms that could be improved in future research. This paper represents a valuable resource for researchers and practitioners interested in knowing the current state of research on the applications of LiDAR in the field of Transportation Engineering while also understanding the opportunities and challenges that lie ahead.  相似文献   

基于改进型BP网络的空间电场分类研究     

张伟  李忠  刘海军  安建琴  宋奕瑶 《地震》2017,37(4):173-180

空间电场信号异常识别是研究地震引起电离层扰动的重要内容。 将空间超低频电场电位数据看作随机数字信号, 以均值、 均方差、 偏度和峰度等四个指标进行描述, 采用“5·12”汶川大地震前空间超低频电场电位数据作为原始数据, 训练改进型BP神经网络, 建立了空间电场信号异常分类识别模型, 并以SOM神经网络进行验证。 计算结果显示, 空间超低频电场电位异常信号主要集中在5°~25°N, 88°~120°E之间的区域, 汶川大地震影响范围内的电离层扰动, 可能是汶川地震发生前引起的, 这与前人研究一致, 说明采用改进型BP神经网络异常分类识别模型研究地震引起的电离层扰动是可行的。  相似文献