基于神经网络模型的湛江湾水体有色溶解有机物的遥感估算     

余果  付东洋  刘大召  刘贝  廖珊  王立安  张小龙 《海洋科学》2018,42(9):73-80

有色溶解有机物(Colored Dissolved Organic Matter, CDOM)是水体中重要的水质参数之一,是水色遥感的重要研究对象,如何构建适合特定区域的近海二类水体CDOM反演模型一直是国内外研究难点。本文利用2017年5月26～29日对南海西北部海域湛江湾20个站位采集的水样和测量的光谱资料,分析归一化遥感反射率与CDOM浓度a_g(400)的相关性,发现最大负相关系数出现在586nm处,选择580、585、590、595nm这四个波段处的归一化遥感反射率与a_g(400)建立了多元线性回归模型、BP(Back-Propagation)神经网络模型和RBF(Radial-Basis Function)神经网络模型,并与其他算法模型进行对比分析。结果发现, BP和RBF神经网络模型的平均相对误差和均方根误差均远小于多元线性回归模型和其他算法模型,神经网络模型的预测值与实测值拟合效果要优于多元线性回归模型。研究表明,神经网络模型更适合于湛江湾有色溶解有机物的遥感估算。  相似文献   

Detecting regional dominant movement patterns in trajectory data with a convolutional neural network     

Can Yang  Győző Gidófalvi 《International journal of geographical information science》2020,34(5):996-1021

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

Lucas May Petry  Camila Leite Da Silva  Andrea Esuli  Chiara Renso  Vania Bogorny 《International journal of geographical information science》2020,34(7):1428-1450

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.  相似文献   

Extracting drainage networks and their connectivity using LiDAR data          下载免费PDF全文

Jennifer Roelens  Ine Rosier  Stefaan Dondeyne  Jos Van Orshoven  Jan Diels 《水文研究》2018,32(8):1026-1037

Policies, measures, and models geared towards flood prevention and managing surface waters benefit from high quality data on the presence and characteristics of drainage ditches. As a cost and labour effective alternative for acquiring such data through field surveys, we propose a method (a) to extract vector data representing ditch drainage networks based on local morphologic features derived from high resolution digital elevation models (DEM) and (b) to identify possible connections in the ditch network by calculating a probability of the connectivity using a logistic regression where the predictor variables are characteristics of the ditch centre lines or derived from the DEM. Using Light Detection and Ranging (LiDAR) derived DEMs with a 1 m resolution, the method was developed and tested for a mixed agricultural residential area in north‐eastern Belgium. The derived ditch segments had an error of omission of 8% and an error of commission of 5%. The original positional accuracy of the centre lines of the extracted ditches was 0.6 m and could be improved to 0.4 m by shifting each vertex to the position of the lowest LiDAR point located within a radius equal to the spatial resolution of the used DEM. About 69% of the false disconnections in the network were identified and corrected leading to a reduction of the unconnected parts of the ditch network by 71%. The extracted and connected network approximated the reference ditch network fairly well.  相似文献   

The information system of the French Peatland Observation Service: Service National d'Observation Tourbières – A valuable tool to assess the impact of global changes on the hydrology and biogeochemistry of temperate peatlands through long term monitoring     

Sébastien Gogo  Jean-Baptiste Paroissien  Fatima Laggoun-Défarge  Jean-Marc Antoine  Léonard Bernard-Jannin  Guillaume Bertrand  Philippe Binet  Stéphane Binet  Guillaume Bouger  Yohann Brossard  Thierry Camboulive  Jean-Pierre Caudal  Stéphane Chevrier  Geneviève Chiapiuso  Benoît D'Angelo  Pilar Durantez  Chris Flechard  André-Jean Francez  Didier Galop  Laure Gandois  Daniel Gilbert  Christophe Guimbaud  Louis Hinault  Adrien Jacotot  Franck Le Moing  Emilie Lerigoleur  Gaël Le Roux  Fabien Leroy  Alexandre Lhosmot  Qian Li  Elodie Machado Da Silva  Jean-Sébastien Moquet  Juanita Mora-Gomez  Laurent Perdereau  Thomas Rosset  Marie-Laure Toussaint 《水文研究》2021,35(6):e14244

Mitigating and adapting to global changes requires a better understanding of the response of the Biosphere to these environmental variations. Human disturbances and their effects act in the long term (decades to centuries) and consequently, a similar time frame is needed to fully understand the hydrological and biogeochemical functioning of a natural system. To this end, the ‘Centre National de la Recherche Scientifique’ (CNRS) promotes and certifies long-term monitoring tools called national observation services or ‘Service National d'Observation’ (SNO) in a large range of hydrological and biogeochemical systems (e.g., cryosphere, catchments, aquifers). The SNO investigating peatlands, the SNO ‘Tourbières’, was certified in 2011 ( https://www.sno-tourbieres.cnrs.fr/ ). Peatlands are mostly found in the high latitudes of the northern hemisphere and French peatlands are located in the southern part of this area. Thus, they are located in environmental conditions that will occur in northern peatlands in coming decades or centuries and can be considered as sentinels. The SNO Tourbières is composed of four peatlands: La Guette (lowland central France), Landemarais (lowland oceanic western France), Frasne (upland continental eastern France) and Bernadouze (upland southern France). Thirty target variables are monitored to study the hydrological and biogeochemical functioning of the sites. They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available ( https://data-snot.cnrs.fr/data-access/ ). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution ( https://zenodo.org/communities/sno_tourbieres_data/ ).  相似文献   

北斗一张网业务管理与服务系统设计与实现     

唐晓霏  车一鸣  孙小超  魏以宽  赵鹏 《测绘通报》2020,(1):142-145

针对北斗一张网体系建设与运营项目的实际需求与现状，整合已有基础设施资源，深入研究挖掘了潜在的信息资源，搭建了一个标准化管理与服务应用统一平台——业务管理与服务系统，提高了系统的移动管理能力、系统管理水平及系统服务效率，改变了传统CORS的管理与运维模式，同时也为海量用户提供了更高效便捷的个性化服务，真正从应用层面上实现了规范化、体系化与便捷化。在这一系统的支撑下，北斗一张网服务体系确立了全新的管理机制，也为北斗未来更广泛地推广应用打下坚实基础。  相似文献   

基于深度学习理论的无人机影像分类     

阳成 《北京测绘》2020,(4):481-484

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。  相似文献   

移动轨迹数据可视化方法总结     

酒心愿  柳林  郭慧 《测绘与空间地理信息》2020,(4):178-181

作为移动社交网络的主体,人们移动带来的位置轨迹不仅记录了人的行为历史,也记录了人与社会的交互活动信息。移动社交网络中位置轨迹数据的分析与利用为解决城市问题提供了一种新的思路。本文概述了轨迹数据可视分析中的几种方法,总结了轨迹数据可视分析研究中存在的问题和面临的挑战。  相似文献   

基于广义回归神经网络的全球表层海水1°×1°二氧化碳分压数据推演     

钟国荣  李学刚  曲宝晓  王彦俊  袁华茂  宋金明 《海洋学报》2020,42(10):70-79

表层海水二氧化碳分压是评估海洋碳源汇强度的关键参数，但其实测数据较少、时空分布极不均匀，导致二氧化碳交换通量的估算有很大的不确定性，海洋源汇特征就不能确切获取。为了解决这个难题，在收集的表层大洋二氧化碳地图(Surface Ocean CO₂ Atlas，SOCAT)实测数据集基础上，运用广义回归神经网络建立二氧化碳分压与经纬度、时间、温度、盐度和叶绿素浓度间的非线性关系，构建了1998?2018年间全球1°×1°经纬度的表层海水二氧化碳分压格点数据，其标准误差为16.93 μatm，平均相对误差为2.97%，优于现有研究中的前反馈神经网络、自组织映射神经网络和机器学习算法等方法。根据构建的数据所绘制的全球表层海水二氧化碳分压的分布与现有研究有较好的一致性。  相似文献   

基于拓扑网络研究海州湾食物网结构与复杂性   总被引：1，自引：0，他引：1  

徐从军  刘阳  程远  徐宾铎  张崇良  任一平  薛莹 《海洋学报》2020,42(4):47-54

针对食物网结构与复杂性的研究有助于深入解析食物网的功能、营养动力和能量转化过程。本文根据2011年3?12月在海州湾及其邻近海域进行的5个航次的渔业资源底拖网调查资料以及胃含物分析数据，基于11个拓扑网络指数，构建了海州湾拓扑网络，研究海州湾食物网的结构与复杂性。结果表明，本文分析的海州湾食物网物种数S为93，连接数L为1 021，每个物种的相互作用数量L/S为10.98，连接性L/S2为0.12；顶级物种、中间物种、基础物种的比例分别为29%、69%和2%；食物网的杂食性指数为87%，连接复杂性指数SC为22.20，特征路径长度ChPath为2.11，聚类系数CC为0.23。通过每个物种的相互作用数量和连接性的研究显示，L/S和L/S2的值都处在正常范围内，所以海州湾食物网的复杂性仍保持较高水平。通过物种比例、杂食性指数、连接复杂性指数、特征路径长度、聚类系数对食物网结构分析，发现海州湾食物网结构处于稳定状态，能够在一定程度上抵御外界环境的扰动，保证生态系统功能的正常运行。通过对海州湾食物网结构与复杂性的研究，将为今后海州湾食物网功能的深入研究以及海州湾渔业资源的科学管理提供重要依据。  相似文献