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Satellite imagery has become a major source for identifying and mapping terrestrial and planetary landforms. However, interpretating landforms and their significance, especially in changing environments, may still be questionable. Consequently, ground truth to check training models, especially in mountainous areas, can be problematic. This paper outlines a decimal format, [dLL], for latitude and longitude geolocation that can be used for model interpretation and validation and in data sets. As data have positions in space and time, [dLL] defined points, as for images, can be associated with metadata as nodes. Together with vertices, metadata nodes help build 'information surfaces' as part of the Digital Earth. This paper examines aspects of the Critical Zone and data integration via the FAIR data principles, data that are; findable, accessible, interoperable and re-usable. Mapping and making inventories of rock glacier landforms are examined in the context of their geomorphic and environmental significance and the need for geolocated ground truth. Terrestrial examination of rock glaciers shows them to be predominantly glacier-derived landforms and not indicators of permafrost. Remote-sensing technologies used to track developing rock glacier surface features show them to be climatically melting glaciers beneath rock debris covers. Distinguishing between glaciers, debris-covered glaciers and rock glaciers over time is a challenge for new remote sensing satellites and technologies and shows the necessity for a common geolocation format to report many Earth surface features. [ABSTRACT FROM AUTHOR]

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate "Transparent Earth". This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide. [ABSTRACT FROM AUTHOR]

Visual 3D reconstruction enables rebuilding 3D scenes from captured images, serving as a fundamental data source for digital earth modeling and intelligent cities. In the foundational step, recent methods leverage learning-based descriptors for image registration and achieve tremendous advances in precision and robustness. However, these methods inevitably execute down sampling towards high-resolution images to fit the needs of neural networks, which leads to precision degradation of feature localization and matching. Thus, we propose GeoRecon: a novel coarse-to-fine visual 3D reconstruction method that optimally utilizes high-resolution images for high-quality visual 3D reconstruction. Firstly, the coarse stage conducts coarse reconstruction from downsampled images by performing neural matching with geometric priors. Secondly, we define the fine-grained stage, proposing a GPU-based algorithm for generating image-patch correspondences based on the neural matching priors to perform fine-grained image registration. Finally, based on the optimized camera poses under this coarse-to-fine paradigm, progressive dense reconstruction leveraging efficient neural radiance fields is proposed to accomplish the high-quality MVS reconstruction. Comparative experiments across various scenarios demonstrate the proposed method’s superior precision, robustness, and reconstruction quality.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital Earth (DE), a technology offering real-time visualisation of Earth’s processes, has shown promising results in aiding decision-making for a sustainable world, raising awareness about individual impacts on our planet, and supporting the United Nations Sustainable Development Goals (UN SDGs) agenda. However, both DE and SDGs face a common obstacle: Data Quality (DQ). This review investigates the challenge of DQ in the context of DE for SDGs and explores how IoT can address this challenge and extend the reach of DE to support SDGs. Furthermore, the study discusses three core aspects; first, the potential of IoT as a data source that supplements satellite data for DE for SDGs, second, the DQ challenge that is specific to an IoT-enabled DE for SDGs is illustrated through scenarios identified from the literature, and third, solutions and perspectives that address the DQ challenge. This study underscores the necessity of addressing the DQ challenge and discusses some potential solutions to foster effective interdisciplinary collaboration, knowledge sharing, and data reusability. The study provides a viewpoint for understanding and addressing the DQ challenge for an IoT-enabled DE for SDGs to support the UN SDGs agenda for a sustainable world by 2030.

The concept of Digital Earth (DE) was formalized by Al Gore in 1998. At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary. Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society. This creates new opportunities and challenges for the realization of DE. ‘What is DE today?’, ‘What could DE be in the future?’, and ‘What is needed to make DE a reality?’. To answer these questions it is necessary to examine DE considering all the technological, scientific, social, and economic aspects, but also bearing in mind the principles that inspired its formulation. By understanding the lessons learned from the past, it becomes possible to identify the remaining scientific and technological challenges, and the actions needed to achieve the ultimate goal of a ‘Digital Earth for all’. This article reviews the evolution of the DE vision and its multiple definitions, illustrates what has been achieved so far, explains the impact of digital transformation, illustrates the new vision, and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.

We present GeoGlue, a novel method using high-resolution UAV imagery for accurate feature matching, which is normally challenging due to the complicated scenes. Current feature detection methods are performed without guidance of geometric priors (e.g., geometric lines), lacking enough attention given to salient geometric features which are indispensable for accurate matching due to their stable existence across views. In this work, geometric lines are firstly detected by a CNN-based geometry detector (GD) which is pre-trained in a self-supervised manner through automatically generated images. Then, geometric lines are naturally vectorized based on GD and thus non-significant features can be disregarded as judged by their disordered geometric morphology. A graph attention network (GAT) is utilized for final feature matching, spanning across the image pair with geometric priors informed by GD. Comprehensive experiments show that GeoGlue outperforms other state-of-the-art methods in feature-matching accuracy and performance stability, achieving pose estimation with maximum rotation and translation errors under 1% in challenging scenes from benchmark datasets, Tanks & Temples and ETH3D. This study also proposes the first self-supervised deep-learning model for curved line detection, generating geometric priors for matching so that more attention is put on prominent features and improving the visual effect of 3D reconstruction.

Satellite imagery has become a major source for identifying and mapping terrestrial and planetary landforms. However, interpretating landforms and their significance, especially in changing environments, may still be questionable. Consequently, ground truth to check training models, especially in mountainous areas, can be problematic. This paper outlines a decimal format, [dLL], for latitude and longitude geolocation that can be used for model interpretation and validation and in data sets. As data have positions in space and time, [dLL] defined points, as for images, can be associated with metadata as nodes. Together with vertices, metadata nodes help build ‘information surfaces’ as part of the Digital Earth. This paper examines aspects of the Critical Zone and data integration via the FAIR data principles, data that are; findable, accessible, interoperable and re-usable. Mapping and making inventories of rock glacier landforms are examined in the context of their geomorphic and environmental significance and the need for geolocated ground truth. Terrestrial examination of rock glaciers shows them to be predominantly glacier-derived landforms and not indicators of permafrost. Remote-sensing technologies used to track developing rock glacier surface features show them to be climatically melting glaciers beneath rock debris covers. Distinguishing between glaciers, debris-covered glaciers and rock glaciers over time is a challenge for new remote sensing satellites and technologies and shows the necessity for a common geolocation format to report many Earth surface features.

The essential aspects of Gore’s “Digital Earth” concept include “georeferenced” data. This paper is concerned with establishing the need for a standardised and common form to locate points on the earth’s surface. Rather than a degree, minute, second (dms) system for latitude/longitude location, a decimal degree system, formatted as a comma separated variable tuple in square brackets, [dLL], is advocated. Values, to an appropriate number of decimal places, can be inserted in text to be computer searchable. The [dLL] also becomes metadata for a data set or as an index in databases or identifiers for images. Various uses of this system are illustrated. The [dLL] allows data to become more “open” via the FAIR data principles: findability, accessibility, interoperability, and reusability. Wider use of [dLL] in the earth sciences is fundamental to collaboration with other disciplines, especially Critical Zone Science. The general use of [dLL] geolocation can be exploited in a wide variety of data analytical methodologies, some of which are outlined, and in earth science and environmental data situations.

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

ABSTRACTThe technological landscape for managing big Earth observation (EO) data ranges from global solutions on large cloud infrastructures with web-based access to self-hosted implementations. EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales: local, national, regional, or global. Several EO data cubes with a geographic focus (“local EO data cubes”) have been implemented. However, their alignment with the Digital Earth (DE) vision and the benefits and trade-offs in creating and maintaining them ought to be further examined. We investigate local EO data cubes from five perspectives (science, business and industry, government and policy, education, communities and citizens) and illustrate four examples covering three continents at different geographic scales (Swiss Data Cube, semantic EO data cube for Austria, DE Africa, Virginia Data Cube). A local EO data cube can benefit many stakeholders and players but requires several technical developments. These developments include enabling local EO data cubes based on public, global, and cloud-native EO data streaming and interoperability between local EO data cubes. We argue that blurring the dichotomy between global and local aligns with the DE vision to access the world’s knowledge and explore information about the planet.

The advent of modern big data platforms has revolutionized archaeology, enabling the discovery of new sites on a planetary scale. This article overviews how these platforms, within the Digital Earth framework, process and analyze vast amounts of remote sensing data. We explore the capabilities of major publicly accessible platforms in integrating diverse geospatial datasets and employing advanced analytical tools. Through case studies, we illustrate the transformative impact of these technologies on archaeological research, highlighting notable discoveries and the role of collaboration and crowdsourcing. Despite challenges such as data quality and computational costs, future advancements promise to enhance our ability to uncover and understand historical heritage. This work underscores the significant potential of big data platforms in advancing archaeological discoveries and enriching our knowledge of the past through the Digital Earth initiative.

Based on an extensive survey of demand (‘needs’) and supply (existing study programs) a framework for transdisciplinary capacity building in Digital Earth concepts, methods and technologies was developed by the authors. In collaboration with universities from the partner countries Armenia and Kyrgyzstan project funding from the Austrian Development Cooperation was acquired to support implementation. Recognizing the need for an explicit spatial approach from disciplines reaching far beyond the traditional ‘geospatial’ subjects like Geography, Geoinformatics, Geodesy or Planning, the DEvision project addresses study programs like Economics, Ecology, Engineering or Journalism through a focused set of foundational modules implemented as a minor subject or certificate. This vision of a Digital Earth is aligned with general digital transformation in society and business, with implementation of sustainable development goals, and a live-long learning paradigm. Based on modern curriculum concepts and pedagogical concepts like active and blended learning, the DEvision initiative provides a set of five modules as teaching and learning resources. These are primarily set up as open educational resources (OER) supporting teachers, and not intended as self-learning media. Instructors within the respective study programs are expected to guide, mentor and monitor students through assigned online learning materials, or using these resources within existing syllabi and curricula. Overall DEvision follows several clearly defined principles: (i) blended learning based on online resources, (ii) instructor-led pathways aligned with students’ majors, (iii) a modern approach to GIS entirely based on cloud platforms, and (iv) orienting students towards a continuous professional development principle based on online communities. [ABSTRACT FROM AUTHOR]

In this interview, scholar–curator Bryan Norton discusses new forms of planetary-scale image-making with artist–researcher Asia Bazydrieva (Geocinema) and media theorist Jussi Parikka. While collaborating with Bazdyrieva during the production of Geocinema's 'Making of Earths', a documentary exploring the Digital Belt and Road Initiative in China, Parikka wrote a new book, Operational Images: From the Visual to the Invisual (2023). In this study, Parikka suggests that the forms of image-making explored in 'Making of Earths' press against the very borders of the visible, possessing a long history with drastic political and social consequences. Drawing on the history of satellite imagery, drone footage and climate models, both Parikka's and Bazydrieva's work present fecund modes of artistic and scholarly engagement with the contemporary creation and dissemination of what Harun Farocki called 'operational images' in his audiovisual work from the early 2000s. In this conversation, Bazdyrieva, Norton and Parikka discuss the ever-increasing ubiquity of these types of images in order to highlight their role in planetary-scale computation systems, the production of scientific knowledge and the ongoing war in Ukraine. [ABSTRACT FROM AUTHOR]

ABSTRACTAs with the fast advances in the technologies of big Earth data and information communication, Web-based 3D GIS system has come a long way from a few years ago. These advances reflect in many aspects of 3D GIS such as higher real-time performance, enhanced interactivity, more realistic 3D visualization effect and improved user interface. This paper aims to present a comprehensive and up-to-date 3D Web GIS for Emergency Response using the current vue.js web application framework and the well-known Cesium API, taking landslide disaster as an example. Building upon recent advances in WebGL technology, we developed a suite of enhanced 3D spatial analysis functions, including interactive route planning, instant text/image/video messaging being incorporated into both 3D WebGL page and mobile GIS applications, and progressive 3D construction and AR visualization using LiDAR and camera over local emergency network or internet. Moreover, professional functions such as landslide susceptibility mapping, landslide monitoring, spatial temporal contingency plan management, landslide information management, personnel and equipment management, and communication are all implemented and integrated in the 3D GIS system. Most of the functions of the system are implemented using open-source projects, which is beneficial to the development of the 3D GIS research community.

The rapid decline of Arctic sea ice has been reminding us the significant impacts caused by global warming. However, the other side of the coin is that this opens a window to utilize the Arctic sea routes in the summer seasons, bringing remarkable economic benefits for ocean transportation between Asia and Europe. However, commercial vessels with low ice classes must tackle substantial environmental challenges in the Arctic sea routes, particularly those caused by variable sea ice, even in the melting seasons. Therefore, the science-based support for shipping safety in the Arctic sea routes is being given more prominence. Emerging satellite remote sensing technology plays a critical role in environmental monitoring in the Arctic. This paper reviews state-of-the-art satellite observations on monitoring sea ice and potential applications on supporting shipping activities in the Arctic Ocean. Moreover, we introduced a recently developed system based on satellite observations to support the safe transportation of Chinese cargo vessels in the Arctic northern sea route, demonstrating the efforts by both the science and business communities to promote the development of the polar silk road.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present GeoGlue, a novel method using high-resolution UAV imagery for accurate feature matching, which is normally challenging due to the complicated scenes. Current feature detection methods are performed without guidance of geometric priors (e.g., geometric lines), lacking enough attention given to salient geometric features which are indispensable for accurate matching due to their stable existence across views. In this work, geometric lines are firstly detected by a CNN-based geometry detector (GD) which is pre-trained in a self-supervised manner through automatically generated images. Then, geometric lines are naturally vectorized based on GD and thus non-significant features can be disregarded as judged by their disordered geometric morphology. A graph attention network (GAT) is utilized for final feature matching, spanning across the image pair with geometric priors informed by GD. Comprehensive experiments show that GeoGlue outperforms other state-of-the-art methods in feature-matching accuracy and performance stability, achieving pose estimation with maximum rotation and translation errors under 1% in challenging scenes from benchmark datasets, Tanks & Temples and ETH3D. This study also proposes the first self-supervised deep-learning model for curved line detection, generating geometric priors for matching so that more attention is put on prominent features and improving the visual effect of 3D reconstruction. [ABSTRACT FROM AUTHOR]

The concept of Digital Earth (DE) was formalized by Al Gore in 1998. At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary. Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society. This creates new opportunities and challenges for the realization of DE. 'What is DE today?', 'What could DE be in the future?', and 'What is needed to make DE a reality?'. To answer these questions it is necessary to examine DE considering all the technological, scientific, social, and economic aspects, but also bearing in mind the principles that inspired its formulation. By understanding the lessons learned from the past, it becomes possible to identify the remaining scientific and technological challenges, and the actions needed to achieve the ultimate goal of a 'Digital Earth for all'. This article reviews the evolution of the DE vision and its multiple definitions, illustrates what has been achieved so far, explains the impact of digital transformation, illustrates the new vision, and concludes with possible future scenarios and recommended actions to facilitate full DE implementation. [ABSTRACT FROM AUTHOR]

Increasing data resources are available for documenting and detecting changes in environmental, ecological, and socioeconomic processes. Currently, data are distributed across a wide variety of sources (e.g. data silos) and published in a variety of formats, scales, and semantic representations. A key issue, therefore, in building systems that can realize a vision of earth system monitoring remains data integration. Discrete global grid systems (DGGSs) have emerged as a key technology that can provide a common multi-resolution spatial fabric in support of Digital Earth monitoring. However, DGGSs remain in their infancy with many technical, conceptual, and operational challenges. With renewed interest in DGGS brought on by a recently proposed standard, the demands of big data, and growing needs for monitoring environmental changes across a variety of scales, we seek to highlight current challenges that we see as central to moving the field(s) and technologies of DGGS forward. For each of the identified challenges, we illustrate the issue and provide a potential solution using a reference DGGS implementation. Through articulation of these challenges, we hope to identify a clear research agenda, expand the DGGS research footprint, and provide some ideas for moving forward towards a scaleable Digital Earth vision. Addressing such challenges helps the GIScience research community to achieve the real benefits of DGGS and provides DGGS an opportunity to play a role in the next generation of GIS.

[Display omitted] The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic, social, and environmental action. A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals (SDGs) is unfortunately limited in many countries due to lack of data. The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs. Big Earth Data, a special class of big data with spatial attributes, holds tremendous potential to facilitate science, technology, and innovation toward implementing SDGs around the world. Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities, and have successfully carried out case studies to demonstrate their utility in sustainability science. This paper presents implementations of Big Earth Data in evaluating SDG indicators, including the development of new algorithms, indicator expansion (for SDG 11.4.1) and indicator extension (for SDG 11.3.1), introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1, and several new high-quality data products, such as global net ecosystem productivity, high-resolution global mountain green cover index, and endangered species richness. These innovations are used to present a comprehensive analysis of SDGs 2, 6, 11, 13, 14, and 15 from 2010 to 2020 in China utilizing Big Earth Data, concluding that all six SDGs are on schedule to be achieved by 2030. [ABSTRACT FROM AUTHOR]

The 17 Sustainable Development Goals present clear directions toward the green transformation being sought by the global community. The SDGs are an integrated framework, with a complex network of interlinkages between the goals, targets and indicators, and they pose wicked problems to society. Consequently, measuring progress and achievements with the SDGs requires the integration of various spatio-temporal datasets from different domains and the synthesis of disciplines to describe a system of systems. The Group on Earth Observations has developed the concept of Essential Variables to describe systems across Societal Benefit Areas that are applicable for this purpose. Digital Earth is a virtual representation of the planet, potentially encompassing all its systems and life forms, including human societies. Designed as a multi-dimensional, multi-scale, multi-temporal, and multi-layer information facility, Digital Earth is a valuable platform that can contribute to the achievement of the SDGs and a green transformation. To that end, a set of Essential SDGs Variables (ESDGVs) for the platform are proposed and cases of implementation and use are introduced.

The aim of this study was the spatial and temporal monitoring of soil erosion patterns in Iran. The G2 model was used, as an appropriate tool to provide the required month-time step country-wide soil loss maps and statistical outputs. The input parameters were derived with field surveys and remote sensing imagery (MODIS, SRTM, SPOT, and Sentinel-2 scenes). An innovation for G2 was the consideration of the snow cover effect on the rainfall erosivity. It was also the first time G2 was used to map such a big country like Iran on a country scale. The mean annual soil erosion in Iran was found to be 16.5 t ha−1, which is equal to about 2.7 billion tons of soil loss. In spatial terms, the highest soil loss values were found in the north, west and southwest part of Iran, in the steep slopes in Alborz and Zagros mountains. In temporal terms, the highest and lowest monthly erosion values of 3.2 and 0.09 t ha−1 correspond to January and July, respectively.

Globe-based Digital Earth (DE) is a promising system that uses 3D models of the Earth for integration, organization, processing, and visualization of vast multiscale geospatial datasets. The growing size and scale of geospatial datasets present significant obstacles to interactive viewing and meaningful visualizations of these DE systems. To address these challenges, we present a novel web-based multiresolution DE system using a hierarchical discretization of the globe on both server and client sides. The presented web-based system makes use of a novel data encoding technique for rendering large multiscale geospatial datasets, with the additional capability of displaying multiple simultaneous viewpoints. Only the data needed for the current views and scales are encoded and processed. We leverage the power of GPU acceleration on the client-side to perform real-time data rendering and dynamic styling. Efficient rendering of multiple views allows us to support multilevel focus+context visualization, an effective approach to navigate through large multiscale global datasets. The client–server interaction as well as the data encoding, rendering, styling, and visualization techniques utilized by our presented system contribute toward making DE more accessible and informative.

The transforming world evokes changes in social, environmental, and economic dimensions, pushed by the digitalisation of many, if not all, aspects of our lives. Satellite Earth observation, while being “digital” from early on, has experienced a boost by digitalisation in recent years, with new trend s of cloud processing, data cube infrastructure, computer vision, machine learning, at unprecedented speeds. This Special Issue on “Digital | Earth | Observation” is dedicated to the fruitful interplay between the Digital Earth concept and Earth observation, embedded in the great technological trends in this field, and demonstrates how this potential can be used in various application contexts.

Searching for a property is inherently a multicriteria spatial decision. The decision is primarily based on three high-level criteria composed of household needs, building facilities, and location characteristics. Location choice is driven by diverse characteristics; including but not limited to environmental factors, access, services, and the socioeconomic status of a neighbourhood. This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a seven-factor classification tool and an assessment of international property websites. Despite the availability of digital earth data, the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making. Based on a case study in Dublin, Ireland, we find that although neighbourhood digital earth data may be readily available to support decision making, the gap persists. We hypothesise that the reason is two-fold. Firstly, there is a technical challenge to transform location data into usable information. Secondly, the market may not wish to provide location information which can be perceived as negative. We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.

The article is devoted to the implementation of Big Data technology in the auto industry and insurance. The article substantiates the need to use this technology in the context of rapidly growing volumes of information, describes the role of big data in modern life and predicts its use in the concept of the future development of the Digital Earth. The current level of Big Data usage in the automotive sector in Russia and around the world is considered. The §author describes the main areas of application of Big Data, such as car insurance using telematics devices, navigation, improving the production process of cars in terms of identifying and eliminating system flaws that are detected during operation, as well as about the growing popularity and at the same time causing heated controversy of the technology of unmanned driving. Statistical data on the number of self-driving cars in countries, the level of their introduction into everyday life, and people's readiness for a new stage of transport development are presented. Separately, the author raises the question of the legal regulation of Big Data, which at this stage is in its infancy and requires careful study, identifies current legal problems that arise in the process of implementing this technology. As a solution to the problem, a number of measures are proposed to fill the existing gap and establish compliance with the rule of law and common principles for the use and protection of Big data.

When creating a Digital Earth, huge amounts of data are generated through remote sensing and various sensors to observe our planet. Big Data has become of particular value in the context of the development of the Digital Earth. At the same time, every day there is more and more information, which creates serious problems for their collection, storage, processing, ensuring their security and legal regulation. The advent of cloud technologies has made it possible to provide computing support for collecting, storing and processing Big Data, while the heterogeneity of the amount of data collected is growing and it becomes difficult to determine their legal regime, jurisdiction and protection in general. The report analyzes the sources of Big Data used for the development of the Digital Earth, the peculiarities of their collection, storage and processing through cloud technologies, analyzes the prospects and risks of the development of the Digital Earth in terms of the security of Big Data and their legal regulation.

The Author justifies the needs of introducing Geo-Informational Systems into the daily activities of The External State Audit Bodies by revealing the concepts of both controlling and digital audit applications. With the development of introducing digital auditing into The Accounting Chamber of The Russian Federation along with Russia’s leadership role in INTOSAI, the time has come to modernize our information systems for the 21st century and beyond.

Healthcare providers such as the World Health Organization, transnational and global health initiatives, the national healthcare systems, down to the smallest villages and individual practitioners and professionals could benefit from geo referential data and metadata and 3D digital assets provided by space technology. Health prevention and literacy programs, mortality and morbidity rates, including contextual statistical data about populations and territories are being already produced and accessible. The hypothetical frame of a Digital Health Earth hereto presented could be performed as the interoperability of 3D representations of sectors of territories and geolocalized layers about health and environment. SDG Goals crossed with WHO programs and available data can become the premises for the design and development of a global representation of healthcare situations, highlighting priorities and disseminating data by intuitive and interactive modes of visualization as it is already happening with 2D dashboards about COVID-19 pandemic. Healthcare practitioners, professionals, health managers, but also patients, proxy, social workers, laypeople, stakeholders and media could benefit from visualizing and comparing Digital Earth health data. Concerns about privacy, digital divide and social exclusion from primary care services and how quality of lives might occur are considered here. As a consequence of Space Technology, especially for its connection with the Satellite industry, Digital Health Earth, will contribute to the development of a new added value economic branch inside the increasing market of the Space Economy Revolution.

The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

The question is posed: "If we use the Digital Earth to verify the results of an audit (e.g. land cadastre) conducted in a traditional format, who can verify the Digital Earth itself?" The answer is, "The Digital Earth can audit itself. Only you have to teach it to do it". The mechanism of self-checking is proposed to be realized through autoreflexion. If Digital Earth is "taught" autoreflexion, there are no obstacles to the transition to peer-to-peer reflexion and multirange reflexion. The reflexive model of the Digital Earth is discussed both in the case of ordinary introspection (auto-audit) and in the case of multiple ranks of reflexion. The way of inclusion of the person in the digital network as a support structure of the Digital Earth is discussed. At the next level of development the reflexive Digital Earth becomes recursive, which means development toward the noosphere as understood by academician Vladimir I. Vernadsky – the thinking Digital Earth. Finally, at the highest stage of development Digital Earth will understand man and humanity.

Geospatial data visualisation and interaction have been cartographic challenges from many perspectives. User experience in this field is tailored to various variables such as the level of detail, the sophistication of the required demonstration, the traditional visualisation paradigm, the volume of the data and the user's domain knowledge. Additionally, in an era of ubiquitous map consumption on various devices, including those in immersive experiences, the context of use stands out as an essential factor for map design. Understanding context involves grasping the interaction between the map user, the map use environment, the map use activity, and the map itself (Griffin et al., 2017). However, efforts have been made to design a user-friendly interface to improve navigation and interaction, but the geospatial data platforms available on demand are still not fully multimodal interfaces. The available platform's design, usability and accessibility are bound to the mouse, keyboard or touchscreen. Multimodal interfaces have been shown to significantly reduce the complexity of GIS interfaces (Fuhrmann et al., 2005), (Oviatt, 1996). Voice input can be considered one of the features that overcome the mentioned obstacles. Furthermore, it can allow users to control their interaction and selection while processing the data. Therefore, the integration of speech recognition technology with map applications can enable users to interact with map data and access information in a more natural and intuitive way without the need for complex user interfaces or manual input. (J. Austerjost, 2018). [ABSTRACT FROM AUTHOR]

The rapid decline of Arctic sea ice has been reminding us the significant impacts caused by global warming. However, the other side of the coin is that this opens a window to utilize the Arctic sea routes in the summer seasons, bringing remarkable economic benefits for ocean transportation between Asia and Europe. However, commercial vessels with low ice classes must tackle substantial environmental challenges in the Arctic sea routes, particularly those caused by variable sea ice, even in the melting seasons. Therefore, the science-based support for shipping safety in the Arctic sea routes is being given more prominence. Emerging satellite remote sensing technology plays a critical role in environmental monitoring in the Arctic. This paper reviews state-of-the-art satellite observations on monitoring sea ice and potential applications on supporting shipping activities in the Arctic Ocean. Moreover, we introduced a recently developed system based on satellite observations to support the safe transportation of Chinese cargo vessels in the Arctic northern sea route, demonstrating the efforts by both the science and business communities to promote the development of the polar silk road. [ABSTRACT FROM AUTHOR]

Geoportals are a consolidated web-based solution to provide open spatial data sharing and online geo-information management. Their roles and possible advancements according to the Digital Earth vision and implementation require investigations. This paper presents a review of the literature concerning geoportals and serves the following primary purposes. First, various geoportal approaches for discovering and accessing Earth observation data and geo-information, mainly with scientific purposes, are summarized according to their characteristics and functionalities. Second, current major challenges in geoportals are identified in terms of functionalities, technologies, and especially big data support, from geoportal cases of China. Finally, based on lessons learned from the international and Chinese geoportals, solutions and recommendations for the challenges in geoportals are proposed in terms of their architectures, services, and technologies. The results show that geoportals usually provide access to distributed data systems, offering maps, data discovery, and data downloads. Some of them are also capable of offering online analysis and processing service, enhanced semantic search engines, and dynamic visualization tools. The strength of geoportals could lead to a full-fledged online Digital Earth system that could provide better data sharing and dissemination solutions to the challenges posed by big data.

The foundation of modern Digital Earth frameworks is the Discrete Global Grid System (DGGS). To standardize the DGGS model, the Open Geospatial Consortium (OGC) recently created the DGGS Abstract Specification, which also aims to increase usability and interoperability between DGGSs. To support these demands and aid future research, open implementations are necessary. However, several OGC conformant DGGSs are not available for researchers to use. This has motivated us to develop an open-source web service that allows users to create quadrilateral grids based on the rHEALPix DGGS. In this paper, we describe the implementation of the web service, including issues and limitations, and demonstrate how discrete global grids and regional grids can be created. Lastly, we present examples that show how vector data sets can be modeled and integrated at different levels of resolution – a key benefit of the DGGS model.

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

Sentinel-2 scenes are increasingly being used in operational Earth observation (EO) applications at regional, continental and global scales, in near-real time applications, and with multi-temporal approaches. On a broader scale, they are therefore one of the most important facilitators of the Digital Earth. However, the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness, the position on the Earth or the acquisition plan. The spatio-temporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider. This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017, enabling the spatio-temporal coverage and availability to be quantified, including scene availability and cloudiness. Spatial exploratory analysis of the global, multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues, e.g. systematically incorrect cloud cover estimation in high, non-vegetated altitudes. The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass. It contributes to a better understanding and selection of Sentinel-2 scenes, and improves the planning and interpretation of remote sensing analyses.

The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before. These changes will unlikely stop or even decelerate in the near future. There is an urgent need for a new scientific approach and an advanced form of evidence-based decision-making towards the benefit of society, the economy, and the environment. To understand the impacts and interrelationships between humans as a society and natural Earth system processes, we propose a new engineering discipline, Big Earth Data science. This science is called to provide the methodologies and tools to generate knowledge from diverse, numerous, and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth. Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves. The manuscript introduces the universe of discourse characterizing this new science, its foundational paradigms and methodologies, and a possible technological framework to be implemented by applying an ecosystem approach. CASEarth and GEOSS are presented as examples of international implementation attempts. Conclusions discuss important challenges and collaboration opportunities.

Almost all causative factors of diseases depend on location. The Digital Earth approach is suitable for studying diseases globally. Geospatial information systems integrated with statistical models can be used to model the relationship between a disease and its causative factors. Through modelling, the most important causative factors can be extracted and the epidemiology of the disease can be observed. In this paper, skin cancer (the most common type of cancer) has been modelled based on its causative factors, including climate factors, people's occupations, nutrition habits, socio-economic factors, and usage of chemical fertiliser. To fit the model, a data framework was first designed, and then data were gathered and processed. Finally, the disease was modelled using Generalised Linear Models (GLM), a statistical model based on the location of the factors. The results of this study identify the most important causative factors together with their relative priority. Furthermore, a model was used to predict the change in skin cancer occurrences caused by a change in one of its causative factors. This work illustrates the ability of the model to predict disease occurrence. Thus, by using this Digital Earth approach, skincancer can be studied in all the key countries around the world.

Many visions for geospatial technology have been advanced over the past half century. Initially researchers saw the handling of geospatial data as the major problem to be overcome. The vision of geographic information systems arose as an early international consensus. Later visions included spatial data infrastructure, Digital Earth, and a nervous system for the planet. With accelerating advances in information technology, a new vision is needed that reflects today’s focus on open and multimodal access, sharing, engagement, the Web, Big Data, artificial intelligence, and data science. We elaborate on the concept of geospatial infrastructure, and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.

The aim of this study was the spatial and temporal monitoring of soil erosion patterns in Iran. The G2 model was used, as an appropriate tool to provide the required month-time step country-wide soil loss maps and statistical outputs. The input parameters were derived with field surveys and remote sensing imagery (MODIS, SRTM, SPOT, and Sentinel-2 scenes). An innovation for G2 was the consideration of the snow cover effect on the rainfall erosivity. It was also the first time G2 was used to map such a big country like Iran on a country scale. The mean annual soil erosion in Iran was found to be 16.5 t ha−1, which is equal to about 2.7 billion tons of soil loss. In spatial terms, the highest soil loss values were found in the north, west and southwest part of Iran, in the steep slopes in Alborz and Zagros mountains. In temporal terms, the highest and lowest monthly erosion values of 3.2 and 0.09 t ha−1 correspond to January and July, respectively. [ABSTRACT FROM AUTHOR]

Digital Earth frameworks provide a way to integrate, analyze, and visualize large volumes of geospatial data, and the foundation of such frameworks is the Discrete Global Grid System (DGGS). One approach in particular, the rHEALPix DGGS, has the rare property of distribution of cell nuclei along rings of constant latitude (or isolatitude rings). However, this property is yet to be explored. In this paper, we extend existing work on the rHEALPix DGGS by proposing a method to determine the isolatitude ring on which the nucleus of a given cell falls by converting a cell identifier to isolatitude ring without recourse to geodetic coordinates. In addition, we present an efficient method to calculate the geodetic latitude of a cell’s nucleus via its associated isolatitude ring. Lastly, we use the proposed methods to demonstrate how the isolatitude property of the rHEALPix DGGS can be utilized to facilitate latitudinal data analysis at multiple resolutions.

The practice of producing drone videos for hobby or commercial purposes has already created a vast amount of open and free video datasets. When these videos are properly authored, time-stamped and geo-referenced, they receive characteristics of volunteered geographic information (VGI). As alternative forms to user-generated content (UGC), these visually appealing footages attract significant attention, but their production faces different practical and motivational issues that could impose limitation on the value of this kind of VGI. In order to better understand volunteered geographic drone videos (VGDV) from the social media and VGI perspective we conceptualize and discuss prospects and problems that could be explored in further research. This paper contributes to the development of theory about aerial drone videos, exploration of aerial drone video UGC characteristics and to the applicability of drone videos in Digital Earth systems.

Broadly, this paper is about designing memorable 3D geovisualizations for spatial knowledge acquisition during (virtual) navigation. Navigation is a fundamentally important task, and even though most people navigate every day, many find it difficult in unfamiliar environments. When people get lost in an unfamiliar environment, or are unable to remember a route that they took, they might feel anxiety, disappointment and frustration; and in real world, such incidents can be costly, and at times, life-threatening. Therefore, in this paper, we study the design decisions in terms of visual realism in a city model, propose a visualization design optimized for route learning, implement and empirically evaluate this design. The evaluation features a navigational route learning task, where we measure short- and long-term recall accuracy of 42 participants with varying spatial abilities and memory capacity. Our findings provide unique empirical evidence on how design choices affect memory in route learning with geovirtual environments, contributing toward empirically verified design guidelines for digital cities.

Virtual reality (VR) is a frequently emphasized issue on the Digital Earth (DE) agenda. While current DE research is more engaged in the technical aspects of VR applications, this paper focuses on what is possible with immersive virtual environments (IVE) from the user's perspective. After a brief discussion of spatial presence and embodiment in the context of IVE, both concepts will be merged into a geovisualization immersion pipeline (GIP) as a framework with which to systematically link technical and cognitive aspects of IVE. We will then analyze the general criteria that must be met by IVE in order to facilitate the experience of spatial presence. Adapting these criteria to the special requirements of geospatial data, a definition of geovisualization immersive virtual environments (GeoIVE) is formulated. Finally, the theoretical considerations of this paper are set into practice, using a GeoIVE of a coral reef ecosystem as an example. As we shall see, while GIS do not provide data models or data compatibility for direct visualization of GeoIVE on VR output devices, game engines can serve as middleware to fill this gap.

The problem of human adaptation to living conditions in the society of the digital revolution is the main content of this study. The change of the communicative paradigm, the essence of social relations and interactions, the problem of critical thinking and the humanitarian basis of classical education is analyzed through the prism of the problem of socio-cultural transformations, social justice, and identification procedures of the modern individual. The article develops the key problem of The XXIV World Congress of Philosophy through the analysis of educational mechanisms of prevention of human nature and spiritual orientations in the face of digital era challenges.

Reminding us of the importance of the narratives and stories we tell each other and their numerous effects on us, Kate Davies, co‐founder of the Unknown Fields travelling design studio, and art and architecture collective Liquid Factory, introduces us to architecture that is a synthesis of the real and virtual worlds that are starting to seamlessly permeate our environment. [ABSTRACT FROM AUTHOR]

Globe-based Digital Earth (DE) is a promising system that uses 3D models of the Earth for integration, organization, processing, and visualization of vast multiscale geospatial datasets. The growing size and scale of geospatial datasets present significant obstacles to interactive viewing and meaningful visualizations of these DE systems. To address these challenges, we present a novel web-based multiresolution DE system using a hierarchical discretization of the globe on both server and client sides. The presented web-based system makes use of a novel data encoding technique for rendering large multiscale geospatial datasets, with the additional capability of displaying multiple simultaneous viewpoints. Only the data needed for the current views and scales are encoded and processed. We leverage the power of GPU acceleration on the client-side to perform real-time data rendering and dynamic styling. Efficient rendering of multiple views allows us to support multilevel focus+context visualization, an effective approach to navigate through large multiscale global datasets. The client–server interaction as well as the data encoding, rendering, styling, and visualization techniques utilized by our presented system contribute toward making DE more accessible and informative. [ABSTRACT FROM AUTHOR]