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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.

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.

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.

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.

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.

Big Earth Data analysis is a complex task requiring the integration of many skills and technologies. This paper provides a comprehensive review of the technology and terminology within the Big Earth Data problem space and presents examples of state-of-the-art projects in each major branch of Big Earth Data research. Current issues within Big Earth Data research are highlighted and potential future solutions identified.

In the past three decades, breakthroughs in satellites and remote sensing have highly demonstrated their potential to characterize and model the various components of the hydrological cycle. A wealth of satellite missions are launched and some of the missions are specifically designed for hydrological research. Given the massive big data for hydrology, it is time for hydrology to embrace the fourth paradigm, data intensive science. This paper aims to highlight available and emergent technologies and missions in the field of Earth observation that have contributed greatly to hydrological science, the current status of those technologies and their improvements in our understanding of hydrological components, and to identify the important and emerging issues in Earth observation data applications in hydrology. This review will provide the readers with detail of Earth observation progress applications in hydrology.

The potential of big data fused with the vision of a digital Earth offers powerful opportunities to deepen understanding of the whole Earth system and the management of a sustainable planet. It is important to stand back from often confusing detail to clarify what those opportunities are and how they might be seized. The essential scientific potential of data, big or small, is to reveal patterns, which have often been the fundamental first step in stimulating inquiry, leading to new questions, new perspectives and potentially to new answers. The digital revolution has created a “digital microscope” that permits us to see patterns that have not been seen before, and when coupled with machine learning technologies to analyse them in creating statistical predictions of the behaviour of both human and non-human systems. These potentials converge with the imperative to represent an Earth system with interacting non-human and human components, as a vital contribution to the understanding and actions required in working towards planetary sustainability. But a digital Earth is also capable of being represented mathematically as a digitally networked phenomenon, analogous to an analogue computer, and should be an important target for a Big Earth Data Journal. We should also return to Al Gore’s vision of an accessible digital Earth with wide usability. Pre-determining the separate functions of parallel digital Earths risks losing one of the great potentials of big data and learning algorithms, the identification and analysis of unanticipated relationships and processes.