论智能化对地观测系统

分析了不同遥感用户对遥感数据的不同需求,提出了解决遥感数据处理自动化、智能化和实时化的方案———智能对地观测系统(IEOS),分析其体系结构和关键技术,并对IEOS的前景做了展望。未来全球IEOS的用足出不出户,就可以通过终端设备,方便获取全球任何空间信息。     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

Big Data and cloud computing: innovation opportunities and challenges

ABSTRACT

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.     

Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data

ABSTRACT

Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models. The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity. Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side. Four practical examples are presented from the marine, climate, planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow. Web service technologies offer a time- and cost-effective way to access multi-dimensional data in a user-tailored format and allow for rapid application development or time-series extraction. Data transport is minimised and enhanced processing capabilities are offered. More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces. At the same time, data users have to become aware of the advantages of web services and be trained how to benefit from them most.     

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Abstract

The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC).     

天地一体化数字影像地理空间信息的获取与更新

数字地理空间信息在经济社会发展、应急救援服务及国防军事中的作用越来越重要。网格(Grid)技术正成为继Internet和Web之后的第三代网络技术，地球空间信息网格体系的发展是对测绘科技发展的又一次机遇，航空／航天数字栅格图像数据能为现代网格地图，或无级无缝在线服务的电子地图提供丰富的动态的数据源。进入21世纪以来，多种方式的天地一体化对地观测技术体系不断发展，能全天时、快速、高精度地获取地理空间的数字影像数据。同时，为了适应不同领域和目标应用的需要，建立或更新GIS数据库的数据，图像数据处理与分析理论和方法的研究也成果斐然。     

Recent Maps and Atlases — Map Cabinet

Abstract

There is currently a high demand for spatial data usage within web applications. From a technical viewpoint, web services and geoportals aim to fulfil user requirements; however, the current cartographic methods do not satisfy the needs of the end users. The problem is more challenging when the final map contains data from various sources that have various cartographic characteristics; therefore, the vital information might be located under the base map layer. In this paper, we propose the concept of layer priorities as foreground, middle ground or background, and we propose the two following methods to enhance the symbolisation: polygon overlay and colour saturation methods. The results from two case studies show that these methods can satisfy the requirements of the end users.     

数字地球网格计算雏议

数字地球将为人类提供关于我们地球的海量自然和人文数据与信息 ,是我们生活的行星的一个多分辨率 ,四维虚拟表达。网格计算被认为是解决数字地球问题的最好方法。数字地球问题的解决必须通过异构的计算资源 ,信息系统 ,设备 ,人之间的相互协作 ,而这些都是地理位置或组织结构分散的。本文介绍我们在生成用于解决数字地球问题的核心中间件的研究工作以及结果。因为网格计算本身是一门比较新的领域 ,网格计算与数字地球的有机结合将为数字地球提供一个全新的计算工具     

Overview and preliminary idea for building Digital Earth with Grid computing technology

Abstract

Digital Earth is an important field of information technology and a research frontier of geosciences in the 21st century. So far, the Grid computing technique is one of the best solutions for Digital Earth infrastructure. Digital Earth can only be realised through the interaction of people, heterogeneous computing resources, information systems, and instruments, all of which are geographically and organisationally dispersed. Earth observation (EO) includes information acquisition, processing and applications. Information acquisition provides a vast amount of spatial data for building the fabric resource infrastructure. Information processing means that spatial information processing middleware is used with large amounts of secure Grid computing resources for real-time processing of all kinds of spatial data. We are currently working on the development of core-middleware for EO data processing and applications for the Digital Earth Prototype System, which is available in the Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences (CAS) The further results will be available soon.     

Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data,information technology and shared knowledge

ABSTRACT

The digital transformation taking place in all areas of life has led to a massive increase in digital data – in particular, related to the places where and the ways how we live. To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multi-source data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP’s pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development.     

我国空间对地观测技术的发展与展望

经过50余年发展,我国空间对地观测技术实现了从无到有、由弱到强等巨大跨域,已在经济建设和国防安全等领域取得了广泛应用。当前,物联网、大数据、云计算、人工智能等飞速发展,空间对地观测技术正在发生深刻变革。为把握技术发展脉络,推动“十四五”我国空间对地观测领域高质量发展,本文系统梳理了我国空间对地观测技术的发展历程,从国际竞争、国内需求和技术革新等方面分析了面临的机遇和挑战,展望了巨型星座建设、海量数据处理、智能融合应用等技术发展方向。     

雷达遥感六十年：四个阶段的发展

雷达遥感问世60年来已经历了4个阶段的发展,其在对地观测中的作用正日益凸显,已经广泛应用于不同领域。4个阶段分别是单波段单极化阶段,多波段多极化阶段,极化和干涉阶段,以及以双/多站或星座、高时序高分宽幅、3维成像为代表的新阶段。本文结合作者长期在雷达遥感领域的研究经历,总结和回顾了雷达遥感的阶段发展和具有里程碑式的代表性技术;从观测技术、数据处理和应用角度阐述了新阶段雷达遥感的发展趋势,以及雷达遥感与人工智能和大数据结合的思考;最后着眼未来,介绍了月基雷达对地观测平台的前瞻性研究。     

Evolution and implementation of the Digital Earth vision,technology and society

Abstract

Digital Earth's framework can be traced to evolutionary threads with historic foundations that fostered the fertile conceptual and technological incubation. These threads incorporate writings, such as those of the visionary engineering-genius, Buckminster Fuller, in conjunction with an array of space age developments in computers, internet and communications, satellites, and education. In 1998, when Vice President Al Gore articulated the Digital Earth Vision, he portrayed the vision based upon myriad technology factors for the intellectual foundation and sparked a worldwide phenomenon that fortuitously included the Chinese leadership's recognition and acceptance. The Beijing Declaration is recognised for its role promulgating the International Digital Earth Symposium series to promote better understanding of the impacts of Digital Earth technology and applications on behalf of all humankind. Combinations of industrial, academic, and government organisations have advanced the technological components necessary for implementing the Digital Earth Vision at a prodigious rate. Commercial leaders, such as Google, have accelerated the influence of large segments of society towards components of the Digital Earth Vision. However, challenges still remain regarding requisite collaboration on international standards for metadata, interoperability, and data formats for space and time that will affect Digital Earth implementation scenarios. Functional requirements for the model Digital Earth geobrowser remain to be fully articulated. The current paper presents an overview of the historical components, the key players on the international scene, the catalytic technological advances, and the societal response to the growth of the Digital Earth community.     

Geo-reCAPTCHA: Crowdsourcing large amounts of geographic information from earth observation data

The reCAPTCHA concept provides a large amount of valuable information for various applications. First, it provides security, e.g., for a form on a website, by means of a test that only a human could solve. Second, the effort of the user for this test is used to generate additional information, e.g., digitization of books or identification of house numbers. In this work, we present a concept for adapting the reCAPTCHA idea to create user-generated geographic information from earth observation data, and the requirements during the conception and implementation are depicted in detail. Furthermore, the essential parts of a Geo-reCAPTCHA system are described, and afterwards transferred, to a prototype implementation. An empirical user study is conducted to investigate the Geo-reCAPTCHA approach, assessing time and quality of the resulting geographic information. Our results show that a Geo-reCAPTCHA can be solved by the users of our study on building digitization in a short amount of time (19.2 s on average) with an overall average accuracy of the digitizations of 82.2%. In conclusion, Geo-reCAPTCHA has the potential to be a reasonable alternative to the typical reCAPTCHA, and to become a new data-rich channel of crowdsourced geographic information.     

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid – the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.     

Enabling Spatially Aware Mobile Applications

Recently, increasing numbers of mobile phones are appearing on the market that feature advanced navigation capabilities: embedded GPS receivers for global positioning, integrated digital compasses for detecting the heading of the device, or accelerometer‐based tilt sensors will potentially enable upcoming and future mobile phones to measure their location and orientation in 3D space. In this paper, we present an application framework for building spatially aware mobile applications – applications that visualize, process or exchange geospatial information – on mobile phones equipped with such features. The core component of the framework is a novel, platform‐independent XML data exchange format for the interface between application server and mobile device that describes the geographic vicinity of the user. The format enables a variety of new mobile interaction styles and user interface types – from traditional text‐based local search and information interfaces to innovative real‐time user interfaces like Geo‐Wands and Smart Compasses.     

论天地一体化的大测绘--地球空间信息学

阐述了天地一体化地球空间信息学的特点,从时空信息获取、加工、管理和服务几个方面进行论述。文中指出,时空信息获取将走向天地一体和全球化,时空信息加工与处理将走向自动化、智能化和实时化,时空信息管理和分发将走向网格化,而时空信息服务将趋向大众化,并可拉动地理信息产业的形成和发展。基于这些特点,展望了天地一体化大测绘概念下国家测绘局的地位与作用。     

点云场景认知模式——泛化点云EI北大核心CSCD

随着传感器技术和观测平台的迅速发展,点云大数据作为新型遥感的主要数据形式,已经逐渐成为场景感知的重要信息载体,并在地质灾害态势感知、自然资源定量调查和道路交通安全服务等国家重大战略需求中发挥了越来越显著的作用。与此同时,点云观测装备和国家重大战略需求的双重驱动促使空间场景从感知迈向了认知,也对认知处理的算法和算力提出了新的要求。为此,本文以点云场景认知的基本框架为线索,分析了多源点云耦合观测的研究现状,总结了点云场景认知处理的关键进展及其在国家重大战略需求中的典型应用,并凝练了点云场景认知当前面临的主要问题。在此基础上,本文聚焦点云场景认知的前沿挑战,避开传统欧氏空间而转到高维张量流形空间进行点云数据处理,提出了“泛化点云”的科学概念和技术框架,为突破点云场景认知处理的算法和算力提供研究思路。     

面向星群的遥感影像智能服务关键问题EI北大核心CSCD

全天时、全天候和全球的遥感信息实时智能服务是对地观测系统建设的目标。近几年来,随着我国高分专项和商业卫星的发展,在轨卫星数量急剧增加,对地观测能力得到极大增强,使得传统的单星和星座卫星系统的运控、接收、处理和应用服务模式面临严峻挑战,亟须统筹规划卫星应用各环节资源,充分发挥多星协同优势,构建统一的遥感影像实时智能服务体系和系统。本文针对遥感星群卫星体系特点和对地观测用户需求特征,开展面向星群的遥感影像智能服务关键问题研究。提出了面向任务的全球多尺度语义描述网格,统筹全球动静态的任务语义描述,在此基础上重点分析了面向星群的自主任务管理、精准动态规划和协同智能处理等关键技术问题,形成集任务描述、任务管控、任务规划、在轨处理、终端分发一体化的星群智能服务技术体系。通过充分发挥星群协同的优势,结合在轨处理和人工智能技术来降低各环节时间延迟,提高数据处理精度,从而实现完全自动化和智能化的近实时星群智能服务,为对地观测的全天时、全天候快速高效智能服务奠定基础。     

Enhancing the OGC WPS interface with GeoPipes support for real-time geoprocessing

ABSTRACT

Real-time geospatial information is used in various applications such as risk management or alerting services. Especially, the rise of new sensing technologies also increases the demand for processing the data in real time. Today’s spatial data infrastructures, however, do not meet the requirements for real-time geoprocessing. The OpenGIS^® Web Processing Service (WPS) is not designed to process real-time workflows. It has some major drawbacks in asynchronous processing and cannot handle (geo) data streams out of the box. In previous papers, we introduced the GeoPipes approach to share spatiotemporal data in real time. We implemented the concept extending the Message Queue and Telemetry Transport (MQTT) protocol by a spatial and temporal dimension, which we call GeoMQTT. In this paper, we demonstrate the integration of the GeoPipes idea in the WPS interface to expose standardized real-time geoprocessing services. The proof of the concept is illustrated in some exemplary real-time geo processes.