From Digitalized to Intelligentized Surveying and Mapping: Fundamental Issues and Research Agenda

Nowadays Surveying and Mapping(S&M)production and services are facing some serious challenges such as real-timization of data acquisition,automation of information processing,and intellectualization of service applications.The main reason is that current digitalized S&M technologies,which involve complex algorithms and models as the core,are incapable of completely describing and representing the diverse,multi-dimensional and dynamic real world,as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models.In order to address these challenges,it is necessary to explore the use of natural intelligence in S&M,and to develop intelligentized S&M technologies,which are knowledge-guided and algorithm-based.This paper first discusses the basic concepts and ideas of intelligentized S&M,and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M,the construction and realization of hybrid intelligent computing paradigm,and the mechanism and path of empowering production.Further research directions are then proposed in the four areas,including knowledge systems,technologies and methodologies,application systems,and instruments and equipments of intelligentized S&M.Finally,some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

基于对象-事件-过程的时空数据模型及其应用

根据地理本体思想,提出和构建了基于对象-事件-过程的面向对象的时空数据模型。该模型基于3个基本地理实体类型:地理对象、地理事件和地理过程,三者基于地理本体的特性,本体化地表达和概括了地理现象和事物。该模型能有效地描述和表达复杂的地理动态现象和事物,能增强性的探讨和表征地理现象发生和发展的内在关联。甚者,鉴于描述和表达三者的侧重点不同,该模型可灵动性地整合或拆分成其它时空数据模型,不失为一种通用的时空数据模型。最后,以动态变化的海冰为例,验证了模型的可行性和有效性。

时空数据模型综述

虽然相对传统的GIS数据模型而言,人们对时空数据模型的研究起步较晚,但是大量的时空数据模型已经被提出了。在综述已有时空数据模型时,就如何区别时空数据模型的异同,众多研究人员和学者更多地是比较模型优缺点或罗列和陈述各模型针对某一案例的实践和应用,而没有阐述模型间的本质区别,没有过多地解释模型扩展及其关联性,也就无法跟踪已有模型的扩展、变异和发展趋势,最终导致在选择模型的应用、实践和整合其他非-时空数据模型时,不能做出最优抉择。与众多相关时空数据模型的综述文献不同,本文进一步提升和归纳已有模型,主要集中叙述众多相关时空数据模型的扩展和关联性,阐述模型间的本质区别,呈现模型的演变过程,从而为人们在应用和实践时空数据模型时提供科学的指导,为模型的扩展和新模型的研究提供理论依据和参考。

Collaborative validation of GlobeLand30:Methodology and practices

30-m Global Land Cover(GLC)data products permit the detection of land cover changes at the scale of most human land activities,and are therefore used as fundamental information for sustainable development,environmental change studies,and many other societal benefit areas.In the past few years,increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products.However,most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries(areas),and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented.In order to promote a comprehensive and collaborative validation of 30-m GLC data products,the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017,to examine and explore its major problems,including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities.With the joint effort of experts and users from 30 GEO member countries or participating organizations,a technical specification for 30-m GLC validation was developed based on the findings and experiences.An on-line validation tool,GLCVal,was developed by integrating land cover validation procedures with the service computing technologies.About 20 countries(regions)have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal.

Disaggregating population data for assessing progress of SDGs:methods and applications

Rapid population growth has had a significant impact on society,economy and environment,which will challenge the achievement of the United Nations Sustainable Development Goals(SDGs).Spatially accurate and detailed population distribution data are essential for measuring the impact of population growth and tracking progress on the SDGs.However,most population data are evenly distributed within administrative units,which seriously lacks spatial details.There are scale differences between the population statistical data and geospatial data,which makes data analysis and needed research difficult.The disaggregation method is an effective way to obtain the spatial distribution of population with greater granularity.It can also transform the statistical population data from irregular administrative units into regular grids to characterize the spatial distribution of the population,and the original population count is preserved.This paper summarizes the research advances of population disaggregation in terms of methodology,ancillary data,and products and discusses the role of spatial disaggregation of population statistical data in monitoring and evaluating SDG indicators.Furthermore,future work is proposed from two perspectives:challenges with spatial disaggregation and disaggregated population as an Essential SDG Variable(ESDGV).

Exploring spatiotemporal patterns of geosocial media data for urban functional zone identification

Urban Functional Zones(UFZs)can be identified by measuring the spatiotemporal patterns of activities that occur within them.Geosocial media data possesses abundant spatial and temporal information for activity mining.Identifying UFZs from geosocial media data aids urban planning,infrastructure,resource allocation,and transportation modernization in the complex urban system.In this work,we proposed an integrated approach by combining the spatiotemporal clustering method with a machine learning classifier.The spatiotemporal clustering method was used to mine the spatiotemporal patterns of activities,of which the distinctive features were extracted as inputs into a machine learning classifier for UFZ identification.The results show that more than 80%of the UFZs can be correctly identified by our proposed method.It reveals that this work serves as a functional groundwork for future studies,facilitating the understanding of urban systems as well as promoting sustainable urban development.

Identifying urban functional zones by capturing multi-spatial distribution patterns of points of interest

Urban Functional Zone(UFZ)identification is vital for urban planning,renewal,and development.Point of Interest(POI),as one of the most popular data in UFZ studies,is transformed into a geo-corpus under specific sampling strategies,which can be used with Natural Language Processing(NLP)technology to extract geo-semantic features and identify UFZs.However,existing studies only capture a single spatial distribution pattern of POIs,while ignoring the other spatial distribution information.In this paper,we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings.Subsequently,random forest model was leveraged to classify UFZs based on those embeddings.A set of combination experiments were designed for performance validation.The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%,with an improvement of 8.5%compared to the baseline methods.The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.

Analytics of big geosocial media and crowdsourced data

Numerous crowdsourcing and social media platforms such as CrowdSpring,Idea Bounty,DesignCrowd,Facebook,Twitter,Flickr,Weibo,WeChat,and Instagram are creating and sharing vast amounts of user-generated content that can reveal timely and useful infor-mation for detecting traffic patterns,mitigating security risks and other types of time-critical events,discovering social structures characteristics,predicting human movement,etc.Crowdsourcing,also known as volunteered geographic information(VGI),has added a new dimension to traditional geospatial data acquisition by providing fine-grained proxy data for human activity research in urban studies(Chen et al.,2016;Niu&Silva,2020).However,analyzing big geosocial media and crowdsourced data brings significant methodological and theoretical challenges due to the uncertain user representability when referring to human behavior in general,the inherent noisy data that requires high-performance cost of preprocessing,and the heterogeneity in quality and quantity of sources.In particular,geosocial media data and their derived metrics can provide valuable insights and policy strategies,but they require a deep understanding of what the metrics actually measure(Zook,2017).All of these underpin complex assessments,not mention-ing the ethnic and privacy issues.Therefore,new sets of methods and tools are required to analyze the big data from crowdsourcing and social media platforms.

Geographic information science in the era of geospatial big data: A cyberspace perspective

The advent of information and communication technology and the Internet of Things have led our society toward a digital era.The proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to virtual.This transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different perspectives.For instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

Towards a collaborative global land cover information service

The need and critical importance of global land cover and change information has been well recognized.Although rich collection of such information has been made available,the lack of necessary information services to support its easy access,analysis and validation makes it difficult to find,evaluate,select and reuse them through well-designed workflows.Aiming at promoting the development of the needed global land cover information services,this paper presents a conceptual framework for developing a Collaborative Global Land Cover Information Service(CoGland),followed by discussions on its implementation strategies.The framework supports connected and shared land cover and change web services around the world to address resource sharing,community service and cross-board collaboration needs.CoGland can benefit several recent international initiatives such as Future Earth,and many societal benefit areas.The paper further proposes that CoGland be developed within the framework of the Group on Earth Observations with the support of a number of key organizations such as the United Nations Expert Committee on Global Geospatial Information Management,the International Society for Photogrammetry and Remote Sensing,and International Society of Digital Earth.It is hoped that this paper can serve as a starting point for further discussions on CoGland developments.

A review of the use of geosocial media data in agent-based models for studying urban systems

Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that they produce emergent patterns such as selforganization and nonlinearity.Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities.However,agent-based models are difficult to effectively optimize and validate without high quality real-world data.Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies.Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems.This paper provides a general overview of concepts,review of recent applications,and discussion of challenges and opportunities in the context of using geosocial media data in agentbased models for urban systems.We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data,given that research moves towards standard guidelines that enable the comparison and effective use of ABMs,and geosocial media data under appropriate circumstances and applications.

Supporting future earth with global geospatial information

Future Earth,initiated by the International Council of Science Unions and presented during the Pla-net under Pressure conference held on 26-29 March 2012,is a global research initiative aiming at developing the knowledge for effective response to the risks and opportunities of global environ-mental change and for supporting transformation towards a sustainable world.

Analytical geospatial Digital Earth

Digital Earth is a virtual representation of the planet,encompassing all its systems and forms,including human societies,manifested as a multidimensional,multiscale,multi-temporal,and multilayer information facility(Goodchild et al.2012).The Digital Earth vision envelops the vast array of technologies,information frameworks,organizational standards,along with applications across a spectrum of disciplines,and many educational and grassroots operational needs(Guo 2008).