移动轨迹的尺度特征

移动轨迹是移动对象在地理空间环境中移动而留下的痕迹。移动轨迹数据是一类特殊的地理时空数据,它具有时间尺度、空间尺度和语义尺度特征。本文首先分析了轨迹数据的空间尺度特征与时间尺度特征,建立了轨迹空间尺度与时间尺度转换关系式;然后论述了轨迹的语义内涵和语义尺度特征,将轨迹语义分为移动对象语义、地理空间环境语义、采集设备语义、移动方式语义,并从地理空间环境语义的角度分析了轨迹的语义多尺度;最后探讨了轨迹语义尺度与时空尺度之间的一致性关系。     

面向LOD的地质资料机构知识库语义扩展方法研究

针对地质资料信息服务过程中,存在信息孤岛和数据共享不够等问题,提出了面向开放关联数据LOD的地质资料机构知识库语义扩展方法,并对方法的框架和关键技术进行了研究。首先,基于DSpace构建地质资料机构知识库,自动实现资源描述框架RDF的存储与转化,与LOD形成统一的元数据描述标准。其次,构建地质资料数据的关联模型,明确数据间的语义关系。最后,采用D2RQ平台实现地质资料机构知识库与LOD数据集的语义关联。该方法将进一步加快语义化地质资料信息服务的步伐。     

基于语义的地理信息分类体系对比分析

地理信息分类体系之间的语义不一致性,被认为是影响地理信息系统实现在语义层次上信息共享的最大障碍.首先,本文在阐述中国地理信息分类体系之间参照模式的基础上,提出了一种基于语义的地理信息分类体系对比分析方法.其次,对中国现行的几种地理信息标准分类体系进行了详细的对比分析.最后,为中国地理信息分类体系的编制和修订提出了一些建议.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.     

基于深度学习的GF-1卫星WFV影像赤潮探测方法

赤潮是我国主要的海洋生态灾害,有效监测赤潮的发生和空间分布对于赤潮的防治具有重要意义。传统的赤潮监测以低空间分辨率的水色卫星为主,但是其对于频发的小规模赤潮存在监控盲区。GF-1卫星WFV影像具有空间分辨率高、成像幅宽大和重访周期短等优点,在小规模赤潮监测中表现出较大的潜力。然而,GF-1卫星WFV影像的光谱分辨率较低,波段少,传统面向水色卫星的赤潮探测方法无法应用于GF-1卫星WFV数据。而且赤潮具有形态多变、尺度不一的特点,难以精确提取。基于此,本文提出了一种面向GF-1卫星WFV影像的尺度自适应赤潮探测网络（SARTNet）。该网络采用双层主干结构以融合赤潮水体的形状特征与细节特征,并引入注意力机制挖掘不同尺度赤潮特征之间的相关性,提高网络对复杂分布赤潮的探测能力。实验结果表明,SARTNet赤潮探测精度优于现有方法,F1分数达到0.89以上,对不同尺度的赤潮漏提和误提较少,且受环境因素的影响较小。     

基于本体的地理信息查询检索方法研究

传统的GIS查询方式和信息检索方式已经难以满足人们对地理信息查询和检索的越来越广泛的需要,为此需要发展新的地理信息查询检索方法。本文将地理本体的思想引入到信息查询应用中,从查询式优化、查询式扩展、可视化与检索结果知识化等方面展开讨论,是对传统查询检索方法的丰富和发展。在此基础上,设计了基于本体的信息检索模型框架,详细并讨论了实现该检索模型的两个关键技术-自然语言处理技术、本体推理技术。     

MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.     

基于GIS的TM数据分类及辅助制图研究

本文初步研究了遥感、GIS和制图一体化实用技术方法。对黄土丘陵区和沙漠地区TM数据进行了特征信息分析;给出了分层分类和GIS辅助分类结果;经模糊推理和人机交互修改,将提“纯”的遥感专题数据作为GIS的动态信息源,对GIS进行扩充与更新;最后在GIS支持下分层提取专题图并进行辅助制图。     

地理空间元数据关联网络的构建

利用资源描述框架（RDF）设计地理空间元数据关联模型,根据地理空间元数据之间的语义关系和语义相关度的计算,以构建以元数据为节点、元数据之间的语义关系为边、语义相关度为权重的关联网络。在这一网络中,一个节点是一个地理空间元数据的资源描述图,包含属性特征（数据来源、空间特征、时间特征、内容）及其关系特征（元数据之间的语义关系、语义相关度）。实验及其分析表明,地理空间元数据关联网络可以有效地支持地理空间数据语义关联检索、推荐等应用,这与传统的基于关键词的元数据检索方式相比,具有更高的准确度。