Preparation and Certification of Biogeochemical Reference Materials

The Institute of Geophysical and Geochemical Exploration in China recently prepared ten biogeochemical certified reference samples GSB1‐10 (CRMs). The samples are primary agricultural products such as rice, wheat, corn, soybean, cabbage, spinach, tea, powder, chicken and apple. Lyophilisation technology and a high‐alumina ball mill were used to process the samples. Fourteen laboratories and hundreds analysts in China participated in the certification analysis. Over ten reliable analytical methods, predominately ICP‐MS, ICP‐AES and INAA, were used for analysing the samples for fifty‐nine elements. A total of 22477 determinations led to 5136 average data sets. Certified values of fifty‐four elements were assigned.     

协同虚拟地理教学环境的设计与实现

探讨了网络技术和虚拟现实技术支持下的协同虚拟地理教学环境,提出了一种协同虚拟地理教学环境的框架模型,并使用Java和Java 3D开发出原型系统.原型系统在以龙卷风为主题的协同虚拟教学中得到实际应用,效果良好.     

中国城市群高质量发展与科技协同创新共同体建设

中国城市群是国家新型城镇化的主体区、国家高质量发展的战略核心区与碳中和的责任区,科技创新驱动对实现城市群的上述主体目标发挥着不可替代的重要作用。如何在新发展格局下依靠科技创新驱动促进中国城市群实现高质量发展和生态环境高水平保护,并逐步建成世界级城市群,是摆在我们面前亟待解决的现实问题。基于这一问题,在第23届中国科学技术协会年会期间,以“科技创新驱动世界级城市群发展与中国城市群高质量崛起”为主题,专门设立了“区域协同发展论坛暨第二届中国城市群发展论坛”,通过应邀的国内外50多位有关城市群及科技协同创新知名专家的系统研讨,普遍认为：全球城市群的发展已进入21世纪的中国新时代,中国城市群高质量发展地位被提升到了前所未有的战略高度,但城市群发展及规划实施中存在着一系列现实问题不容忽视,未来中国城市群高质量发展必须依靠科技创新驱动,把建设创新驱动的智慧型、碳中和型、双循环型和城乡融合型城市群作为中国城市群高质量发展的基本方向。进一步创新研究工作机制,建立协同创新共同体,逐步把中国城市群建成为高水平开放、高质量发展、高品质生活和高效能治理的世界级城市群。     

地理时空大数据协同计算技术

大数据时代,地理时空数据的迅猛增长给应用理念、技术框架和服务形式带来挑战。本文在阐述地理时空大数据概念的基础上,首先分析了地理时空大数据计算面临的挑战,从数据协同、技术协同、服务协同和生产协同4个层次阐述了地理时空大数据协同计算方法;然后,根据平台化服务的需求设计了地理时空大数据协同计算框架,从遥感数据综合预处理、地理时空数据的组织与管理、地理时空大数据高效计算、地理时空大数据可视化4个方面论述了地理时空大数据协同计算实现的关键技术;最后,以遥感大数据综合处理系统作为案例说明了地理时空大数据协同计算与服务的实现方法,并对地理时空大数据的应用模式进行了展望。     

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

Travelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this paper, we define users’ travel behaviours from their historical Global Positioning System (GPS) trajectories and propose two personalized travel route recommendation methods – collaborative travel route recommendation (CTRR) and an extended version of CTRR (CTRR+). Both methods consider users’ personal travel preferences based on their historical GPS trajectories. In this paper, we first estimate users’ travel behaviour frequencies by using collaborative filtering technique. A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. This paper also conducts some case studies based on a real GPS trajectory data set from Beijing, China. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method.     

FedIDS: a federated cloud storage architecture and satellite image delivery service for building dependable geospatial platforms

Earth observation satellites produce large amounts of images/data that not only must be processed and preserved in reliable geospatial platforms but also efficiently disseminated among partners/researchers for creating derivative products through collaborative workflows. Organizations can face up this challenge in a cost-effective manner by using cloud services. However, outages and violations of integrity/confidentiality associated to this technology could arise. This article presents FedIDS, a suite of cloud-based components for building dependable geospatial platforms. The Fed component enables organizations to build shared geospatial data infrastructure through federation of independent cloud resources to withstand outages, whereas IDS avoids violations of integrity/confidentiality of images/data in sharing information and collaboration workflows. A FedIDS prototype, deployed in Spain and Mexico, was evaluated through a study case based on a satellite imagery captured by a Mexican antenna and another based on a satellite imagery of a European observation mission. The acquisition, storage and sharing of images among users of the federation, the exchange of images between Mexican and Spanish sites and outage scenarios were evaluated. The evaluation revealed the feasibility, reliability and efficiency of FedIDS, in comparison with available solutions, in terms of performance, storage consume and integrity/confidentiality when sharing images/data in collaborative scenarios.     

大气环境卫星污染气体和大气颗粒物协同观测综述

空气污染作为一种重要的环境问题,直接影响人们的日常生活和身体健康。随着污染气体和颗粒物观测技术的逐步成熟,基于卫星平台的近地层大气污染物监测得到了快速的发展。本文概括性描述了大气环境关注的污染气体和大气颗粒物的主流遥感方法,对各方法的适用场景及优缺点进行了评述。尽管差分吸收光谱方法对污染气体的监测十分有效,但最优估计算法可进一步从多光谱信息中提取部分污染气体（例如：臭氧O3、一氧化碳CO等）的分层信息,有助于更细致地刻画污染气体在整层大气中的垂直分布。对于大气颗粒物遥感方法,采用不同的技术手段进行地气解耦是算法的核心问题,增加光谱、角度、偏振以及时间序列信息都可有效增加算法的地气解耦能力。基于对污染气体和大气颗粒物反演算法的总结,从污染气体和颗粒物协同观测的角度对卫星平台及传感器的发展历程进行了梳理,论述了紫外、红外以及可见光波段的传感器协同观测的优势,展望了未来静止卫星星座的高时空分辨率大气污染监测能力以及中国卫星的贡献。还探讨了以近地面大气污染物监测卫星探测技术及遥感算法亟待解决的问题以及未来的可能发展方向。