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学者姓名：方莉娜
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城市出租汽车是居民出行的重要方式之一，地理流空间理论为发掘人群出行特征，优化车辆运营效率提供了新视角。本文利用厦门市出租汽车轨迹数据，采用地理流空间分析理论，对人群出行的整体随机性质进行了分析，基于流相似性度量识别并分析了丛集、汇聚、发散和社区4种典型模式及混合模式的空间分布特征，对比了基于巡游车和网约车2种车辆的人群出行模式。结果表明流空间理论能够系统性发现人群出行典型模式及混合模式，主要体现在：(1)基于2类车辆的人群出行流在空间中呈现出显著的非随机特征；(2)巡游车和网约车的典型模式在空间分布上有明显差别，网约车的有关模式分布范围更广，在厦门岛外各区中心及岛内东部软件园等区域附近较为突出，且网约车由于其订单由用户需求驱动，更容易发现潜在的高出行需求区域，同时出行结构更容易形成社区模式，而巡游车主要分布在传统岛内知名城市地标附近；(3)同一区域内巡游车和网约车出行混合模式普遍存在，约占典型模式的四分之一左右，而且不同类型车辆的主要混合模式存在差异，综合考虑混合模式能够提高城市公共设施规划的精确性和科学性。本文结果可以为车辆调度优化和城市交通规划提供支持，也表明地理流空间理论能够更有效揭示地理流对象的空间模式特征。
Keyword ：
人类移动性 人类移动性 出租车 出租车 出行流模式 出行流模式 地理流空间 地理流空间 流聚类 流聚类 混合流模式 混合流模式 网约车 网约车 轨迹数据 轨迹数据
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| GB/T 7714 | 王鹏洲 , 赵志远 , 姚伟 et al. 基于地理流空间的巡游车与网约车人群出行模式研究 [J]. | 地球信息科学学报 , 2023 , 25 (04) : 726-740 . |
| MLA | 王鹏洲 et al. "基于地理流空间的巡游车与网约车人群出行模式研究" . | 地球信息科学学报 25 . 04 (2023) : 726-740 . |
| APA | 王鹏洲 , 赵志远 , 姚伟 , 吴升 , 汪艳霞 , 方莉娜 et al. 基于地理流空间的巡游车与网约车人群出行模式研究 . | 地球信息科学学报 , 2023 , 25 (04) , 726-740 . |
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Traditional taxis and the e-hailing cars are two main transport vehicles for the public in current taxi market, which aim to satisfy the customized travel demand in daily lives of citizens in urban public transportation system. Due to the differences in service modes and commercial patterns, the two vehicles are appropriate for different target groups. Investigating the spatial and temporal characteristics of these two types of vehicle based on human travel flows can support the applications such as optimization of the urban public transportation and land use planning. The geographical flow space theory proposed recently provides a new theoretical perspective as well as a systematical analysis framework in studying the flow patterns of the travels by different types of vehicles. In this paper, we adopt this formulated theory framework to describe the travel flow. We select five typical flow patterns, namely random, clustering, aggregation, divergence, and community patterns, to reveal their spatial distribution characteristics and compare the differences in their travel patterns. The trajectory dataset of traditional taxis and the e- hailing cars in Xiamen City is employed to validate the effectiveness of the geographical flow space theory. We find that: (1) the travel flows of the two types of vehicles present significant non-random characteristics in flow space; (2) people tend to choose e-hailing cars for long distance travel, while prefer the traditional taxis for short and medium distance travels; (3) the two types of cars show different spatial distribution characteristics of the four typical flow patterns. The travels by e- hailing cars are more widely distributed and exhibit clustering patterns around the sub-centers at the suburban areas outside the core Xiamen Island and the east-southern software park area inside the Xiamen Island. Due to the travel demand driven model, the e-hailing cars satisfy the emerging high travel demand areas and tend to form community patterns. While the traditional cars are mainly distributed around the well-known city landmarks (e.g., Zengcuoan, Zhongshan road) on the Island; (4) approximately a quarter of the local areas have more than one typical flow patterns. Different types of cars exhibit different co-location flow patterns and spatial distribution characteristics. The mixed flow patterns derived from the geographical flow theory provide a more comprehensive perspective to better understand the travel flows, which can mitigate the misleading information from each isolated flow pattern. The above findings imply that the geographical flow theory can help to better understand the characteristics of the geographical flows and can be used to improve the applications based on related results. © 2023 Research Institute of Beijing. All rights reserved.
Keyword ：
e-hailing taxis e-hailing taxis flow clustering flow clustering geographical flow space geographical flow space human mobility human mobility mixed flow patterns mixed flow patterns traditional taxis traditional taxis trajectory data trajectory data travel flow patterns travel flow patterns
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| GB/T 7714 | Wang, P. , Zhao, Z. , Yao, W. et al. Human Travel Patterns by E-hailing Cars and Traditional Taxis based on Geographic Flow Space; [基于地理流空间的巡游车与网约车人群出行模式研究] [J]. | Journal of Geo-Information Science , 2023 , 25 (4) : 726-740 . |
| MLA | Wang, P. et al. "Human Travel Patterns by E-hailing Cars and Traditional Taxis based on Geographic Flow Space; [基于地理流空间的巡游车与网约车人群出行模式研究]" . | Journal of Geo-Information Science 25 . 4 (2023) : 726-740 . |
| APA | Wang, P. , Zhao, Z. , Yao, W. , Wu, S. , Wang, Y. , Fang, L. et al. Human Travel Patterns by E-hailing Cars and Traditional Taxis based on Geographic Flow Space; [基于地理流空间的巡游车与网约车人群出行模式研究] . | Journal of Geo-Information Science , 2023 , 25 (4) , 726-740 . |
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Currently, mobile laser scanning (MLS) systems can conveniently and rapidly measure the backscattered laser beam properties of the object surfaces in large-scale roadway scenes. Such properties is digitalized as the in-tensity value stored in the acquired point cloud data, and the intensity as an important information source has been widely used in a variety of applications, including road marking inventory, manhole cover detection, and pavement inspection. However, the collected intensity is often deviated from the object reflectance due to two main factors, i.e. different scanning distances and worn-out surfaces. Therefore, in this paper, we present a new intensity-enhanced method to gradually and efficiently achieve the intensity enhancement in the MLS point clouds. Concretely, to eliminate the intensity inconsistency caused by different scanning distances, the direct relationship between scanning distance and intensity value is modeled to correct the inconsistent intensity. To handle the low contrast between 3D points with different intensities, we proposed to introduce and adapt the dark channel prior for adaptively transforming the intensity information in point cloud scenes. To remove the isolated intensity noises, multiple filters are integrated to achieve the denoising in the regions with different point densities. The evaluations of our proposed method are conducted on four MLS datasets, which are acquired at different road scenarios with different MLS systems. Extensive experiments and discussions demonstrate that the proposed method can exhibit the remarkable performance on enhancing the intensities in MLS point clouds.
Keyword ：
Dark Channel Prior Dark Channel Prior Intensity Enhancement Intensity Enhancement Mobile Laser Scanning Mobile Laser Scanning Point Cloud Point Cloud Point Cloud Denoising Point Cloud Denoising
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| GB/T 7714 | Fang, Lina , Chen, Hao , Luo, Huan et al. An intensity-enhanced method for handling mobile laser scanning point clouds [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 107 . |
| MLA | Fang, Lina et al. "An intensity-enhanced method for handling mobile laser scanning point clouds" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 107 (2022) . |
| APA | Fang, Lina , Chen, Hao , Luo, Huan , Guo, Yingya , Li, Jonathon . An intensity-enhanced method for handling mobile laser scanning point clouds . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 107 . |
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The category of road marking is a crucial element in Mobile laser scanning systems' (MLSs) applications such as intelligent traffic systems, high-definition maps, location and navigation services. Due to the complexity of road scenes, considerable and various categories, occlusion and uneven intensities in MLS point clouds, finely road marking classification is considered as the challenging work. This paper proposes a graph attention network named GAT_SCNet to simultaneously group the road markings into 11 categories from MLS point clouds. Concretely, the proposed GAT_SCNet model constructs serial computable subgraphs and fulfills a multi-head attention mechanism to encode the geometric, topological, and spatial relationships between the node and neighbors to generate the distinguishable descriptor of road marking. To assess the effectiveness and general-ization of the GAT_SCNet model, we conduct extensive experiments on five test datasets of about 100 km in total captured by different MLS systems. Three accuracy evaluation metrics: average Precision, Recall, and F-1 of 11 categories on the test datasets exceed 91%, respectively. Accuracy evaluations and comparative studies show that our method has achieved a new state-of-the-art work on road marking classification, especially on similar linear road markings like stop lines, zebra crossings, and dotted lines.
Keyword ：
Attention mechanism Attention mechanism Deep learning Deep learning Graph neural network Graph neural network MLS points clouds MLS points clouds Road marking classification Road marking classification
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| GB/T 7714 | Fang, Lina , Sun, Tongtong , Wang, Shuang et al. A graph attention network for road marking classification from mobile LiDAR point clouds [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 108 . |
| MLA | Fang, Lina et al. "A graph attention network for road marking classification from mobile LiDAR point clouds" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 108 (2022) . |
| APA | Fang, Lina , Sun, Tongtong , Wang, Shuang , Fan, Hongchao , Li, Jonathan . A graph attention network for road marking classification from mobile LiDAR point clouds . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 108 . |
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Urban management and survey departments have begun investigating the feasibility of acquiring data from various laser scanning systems for urban infrastructure measurements and assessments. Roadside objects such as cars, trees, traffic poles, pedestrians, bicycles and e-bicycles describe the static and dynamic urban information available for acquisition. Because of the unstructured nature of 3D point clouds, the rich targets in complex road scenes, and the varying scales of roadside objects, finely classifying these roadside objects from various point clouds is a challenging task. In this paper, we integrate two representations of roadside objects, point clouds and multiview images to propose a point-group-view network named PGVNet for classifying roadside objects into cars, trees, traffic poles, and small objects (pedestrians, bicycles and e-bicycles) from generalized point clouds. To utilize the topological information of the point clouds, we propose a graph attention convolution operation called AtEdgeConv to mine the relationship among the local points and to extract local geometric features. In addition, we employ a hierarchical view-group-object architecture to diminish the redundant information between similar views and to obtain salient viewwise global features. To fuse the local geometric features from the point clouds and the global features from multiview images, we stack an attention-guided fusion network in PGVNet. In particular, we quantify and leverage the global features as an attention mask to capture the intrinsic correlation and discriminability of the local geometric features, which contributes to recognizing the different roadside objects with similar shapes. To verify the effectiveness and generalization of our methods, we conduct extensive experiments on six test datasets of different urban scenes, which were captured by different laser scanning systems, including mobile laser scanning (MLS) systems, unmanned aerial vehicle (UAV)-based laser scanning (ULS) systems and backpack laser scanning (BLS) systems. Experimental results, and comparisons with state-of-the-art methods, demonstrate that the PGVNet model is able to effectively identify various cars, trees, traffic poles and small objects from generalized point clouds, and achieves promising performances on roadside object classifications, with an overall accuracy of 95.76%. Our code is released on https://github.com/flidarcode/ PGVNet.
Keyword ：
Attention mechanism Attention mechanism Deep learning Deep learning Mobile laser scanning systems Mobile laser scanning systems Multiview images Multiview images Point cloud classification Point cloud classification
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| GB/T 7714 | Fang, Lina , You, Zhilong , Shen, Guixi et al. A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2022 , 193 : 115-136 . |
| MLA | Fang, Lina et al. "A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 193 (2022) : 115-136 . |
| APA | Fang, Lina , You, Zhilong , Shen, Guixi , Chen, Yiping , Li, Jianrong . A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2022 , 193 , 115-136 . |
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Nowadays, researchers have developed various deep neural networks for processing point clouds effectively. Due to the enormous parameters in deep learning-based models, a lot of manual efforts have to be invested into annotating sufficient training samples. To mitigate such manual efforts of annotating samples for a new scanning device, this letter focuses on proposing a new neural network to achieve domain adaptation in 3-D object classification. Specifically, to minimize the data discrepancy of intraclass objects in different domains, an Asymmetrical Siamese (AS) module is designed to align the intraclass features. To preserve the discriminative information for distinguishing interclass objects in different domains, a Conditional Adversarial (CA) module is leveraged to consider the classification information conveyed from the classifier. To verify the effectiveness of the proposed method on object classification in heterogeneous point clouds, evaluations are conducted on three point cloud datasets, which are collected in different scenarios by different laser scanning devices. Furthermore, the comparative experiments also demonstrate the superior performance of the proposed method on the classification accuracy.
Keyword ：
3-D object classification 3-D object classification asymmetrical Siamese (AS) network asymmetrical Siamese (AS) network Data mining Data mining domain adaptation domain adaptation feature alignment feature alignment Feature extraction Feature extraction Generators Generators Neural networks Neural networks Point cloud compression Point cloud compression point clouds point clouds Three-dimensional displays Three-dimensional displays Training Training
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| GB/T 7714 | Luo, Huan , Li, Lingkai , Fang, Lina et al. Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| MLA | Luo, Huan et al. "Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19 (2022) . |
| APA | Luo, Huan , Li, Lingkai , Fang, Lina , Wang, Hanyun , Wang, Cheng , Guo, Wenzhong et al. Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
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城市环境中的行道树、车辆、杆状交通设施是重要的交通地物,也是智能交通,导航与位置服务,自动驾驶和高精地图等行业应用的核心要素。为了准确识别这些路侧目标,本文提出一种融合点云和多视角图像的深度学习模型PGVNet(point-group-view network),充分利用目标点云数据中空间几何信息及其多视角图像中高级全局特征提升路侧行道树、车辆和杆状设施的分类精度。为了减少视图间的冗余信息并增强显著视图特征,PGVNet模型利用预训练的VGG网络提取多视图特征,对其进行分组赋权获取最优视图特征;采用嵌入注意力机制的融合策略,利用最优视图特征动态调整PGVNet模型对点云不同局部关系的注意力度,...
Keyword ：
多视角图像 多视角图像 注意力机制 注意力机制 深度学习 深度学习 点云分类 点云分类 车载激光扫描系统 车载激光扫描系统
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| GB/T 7714 | 方莉娜 , 沈贵熙 , 游志龙 et al. 融合点云和多视图的车载激光点云路侧多目标识别 [J]. | 测绘学报 , 2021 , 50 (11) : 1558-1573 . |
| MLA | 方莉娜 et al. "融合点云和多视图的车载激光点云路侧多目标识别" . | 测绘学报 50 . 11 (2021) : 1558-1573 . |
| APA | 方莉娜 , 沈贵熙 , 游志龙 , 郭迎亚 , 付化胜 , 赵志远 et al. 融合点云和多视图的车载激光点云路侧多目标识别 . | 测绘学报 , 2021 , 50 (11) , 1558-1573 . |
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Road markings are important traffic safety facilities. Its location, attribute, and topological relationship finely describe road traffic structure, and it is the basic data for applications such as intelligent traffic, high-precision maps, location, and navigation. This paper proposes a graph attention network with spatial context information (GAT_SCNet) to classify the road markings from mobile LiDAR point clouds. GAT_SCNet explores the graph structure to establish the appearance and dependence information among road markings. Meanwhile, GAT_SCNet incorporates the multi-head attention mechanism into the node propagation step, which computes the hidden states of each node based on the geometric, topological, and spatial structure relationships of the neighboring nodes. Finally, road markings classification is realized by the classification of nodes. Then, some schemes are designed for road markings vectorization. Four test datasets consisting of urban and highway scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision and recall of 9 types of road markings on the selected test datasets achieve (100.00%, 93.77%, 100.00%, 100.00%, 100.00%, 96.73%, 97.96%, 100.00%, 98.39%) and (100.00%, 96.36%, 100.00%, 10.000%, 100.00%, 97.26%, 85.72%, 100.00%, 94.16%), respectively. Accuracy evaluations and comparative studies prove that the proposed method has the capability of classifying multi-type road markings simultaneously and distinguishing similar road markings like dashed markings, zebra crossings, and stop lines in complex urban scenes. © 2021, Surveying and Mapping Press. All right reserved.
Keyword ：
Classification (of information) Classification (of information) Graphic methods Graphic methods Highway markings Highway markings Road and street markings Road and street markings Roads and streets Roads and streets Topology Topology
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| GB/T 7714 | Fang, Lina , Wang, Shuang , Zhao, Zhiyuan et al. Automatic classification and vectorization of road markings from mobile laser point clouds [J]. | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (9) : 1251-1265 . |
| MLA | Fang, Lina et al. "Automatic classification and vectorization of road markings from mobile laser point clouds" . | Acta Geodaetica et Cartographica Sinica 50 . 9 (2021) : 1251-1265 . |
| APA | Fang, Lina , Wang, Shuang , Zhao, Zhiyuan , Fu, Huasheng , Chen, Chongcheng . Automatic classification and vectorization of road markings from mobile laser point clouds . | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (9) , 1251-1265 . |
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Accurately identifying roadside objects like trees, cars, and traffic poles from mobile LiDAR point clouds is of great significance for some applications such as intelligent traffic systems, navigation and location services, autonomous driving, and high precision map. In the paper, we proposed a point-group-view network (PGVNet) to classify the roadside objects into trees, cars, traffic poles, and others, which utilize and fuse the advanced global features of multi-view images and the spatial geometry information of point cloud. To reduce redundant information between similar views and highlight salient view features, the PGVNet model employs a hierarchical view-group-shape architecture to split all views into different groups according to their discriminative level, which uses the pre-trained VGG network as the bone network. In view-group-shape architecture, global-level significant features are further generated from group descriptors with their weights. Moreover, an attention-guided fusion network is used to fuse the global features from multi-view images and local geometric features from point clouds. In particular, the global advanced features from multi-view images are quantified and leveraged as the attention mask to further refine the intrinsic correlation and discriminability of the local geometric features from point clouds, which contributions to recognize the roadside objects. We have evaluated the proposed method on five different mobile LiDAR point cloud data. Five test datasets of different urban scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision, recall, quality and Fscore of trees, cars and traffic poles on the selected testing datasets achieve (99.19%, 94.27%, 93.58%, 96.63%), (94.20%, 97.56%, 92.02%, 95.68%), (91.48%, 98.61%, 90.39%, 94.87%), respectively. Experimental results and comparisons with state-of-the-art methods demonstrate that the PGVNet model is available to effectively identify roadside objects from the mobile LiDAR point cloud, which can provide data support for elements construction and vectorization in high precision map applications. © 2021, Surveying and Mapping Press. All right reserved.
Keyword ：
Classification (of information) Classification (of information) Deep learning Deep learning Forestry Forestry Geometry Geometry Laser applications Laser applications Optical radar Optical radar Poles Poles Quality control Quality control Roadsides Roadsides
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| GB/T 7714 | Fang, Lina , Shen, Guixi , You, Zhilong et al. A joint network of point cloud and multiple views for roadside objects recognition from mobile laser point clouds [J]. | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (11) : 1558-1573 . |
| MLA | Fang, Lina et al. "A joint network of point cloud and multiple views for roadside objects recognition from mobile laser point clouds" . | Acta Geodaetica et Cartographica Sinica 50 . 11 (2021) : 1558-1573 . |
| APA | Fang, Lina , Shen, Guixi , You, Zhilong , Guo, Yingya , Fu, Huasheng , Zhao, Zhiyuan et al. A joint network of point cloud and multiple views for roadside objects recognition from mobile laser point clouds . | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (11) , 1558-1573 . |
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道路交叉口是道路交通网的重要组成部分,其位置和类型是高精地图、自动驾驶等应用服务的基础数据.目前研究多关注车载激光点云的道路边界提取,较少关注道路交叉口类型识别.为此,本文提出一种基于动态图神经网络的道路交叉口分类方法.首先分析地面超体素的几何和空间分布差异进行提取道路边界点;然后计算道路边界点曲率,利用滑动窗口中曲率差异检测道路交叉口;最后构建动态图神经网络识别出"T"型和"十"型道路交叉口.实验采用两份不同车载激光点云数据验证本文方法的有效性,实验结果表明,该方法能准确检测绝大多数道路交叉口位置及类型.
Keyword ：
图神经网络 图神经网络 深度学习 深度学习 点云 点云 车载激光扫描系统 车载激光扫描系统 道路交叉口 道路交叉口
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| GB/T 7714 | 方莉娜 , 王康 . 基于车载激光点云的道路交叉口检测与识别 [J]. | 南京信息工程大学学报(自然科学版) , 2021 , 13 (06) : 635-644 . |
| MLA | 方莉娜 et al. "基于车载激光点云的道路交叉口检测与识别" . | 南京信息工程大学学报(自然科学版) 13 . 06 (2021) : 635-644 . |
| APA | 方莉娜 , 王康 . 基于车载激光点云的道路交叉口检测与识别 . | 南京信息工程大学学报(自然科学版) , 2021 , 13 (06) , 635-644 . |
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