Big Earth Data Research Articles

The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition

Over the last decades, to better proceed towards global and local policy goals, there was an increasing demand for the scientific community to support decision-makers with the best available knowledge. Scientific modeling is key to enable the transition from data to knowledge, often requiring to process big datasets through complex physical or empirical (learning-based AI) models. Although cloud technologies provide valuable solutions for addressing several of the Big Earth Data challenges, model sharing is still a complex task. The usual approach of sharing models as services requires maintaining a scalable infrastructure which is often a very high barrier for potential model providers. This paper describes the Virtual Earth Laboratory (VLab), a software framework orchestrating data and model access to implement scientific processes for knowledge generation. The VLab lowers the entry barriers for both developers and users. It adopts mature containerization technologies to access models as source code and to rebuild the required software environment to run them on any supported cloud. This makes VLab fitting in the multi-cloud landscape, which is going to characterize the Big Earth Data analytics domain in the next years. The VLab functionalities are accessible through APIs, enabling developers to create new applications tailored to end-users.

EarthDataMiner: A Cloud-Based Big Earth Data Intelligence Analysis Platform

Big Earth data mining analysis involves complex data processing, pattern extraction and knowledge discovery processes, and requires easy to use integrated software tools to deal with large-scale data. With the support of Big Earth Data Science Engineering Project (CASEarth) of the Chinese Academy of Sciences Strategic Priority Research Program, our team are ongoing developing EarthDataMiner, a cloud-based Big Earth data intelligence analysis platform for scientists in the field of Earth sciences, which brings together cross-domain common models, introduces advanced artificial intelligence algorithms, supports online satellite imagery visually interactive analysis,and achieves efficient task processing through a series of distributed execution systems.

The 1st China Digital Earth Conference

List of Chair, Editors, Contents, Method and Technology of big earth data, Applications of big earth data, Atmosphere, Polar regions, Land Cover, Vegetation and Agriculture and City Development are available in this pdf. Preface The 1st China Digital Earth Conference November 18 – 20, 2019The 1st China Digital Earth Conference (CDEC) was successfully convened in Beijing, China, from November 18 – 20, 2019. The conference was hosted by the International Society for Digital Earth and the Chinese Academy of Sciences.Since the Chinese Academy of Sciences co-sponsored nineteen ministries and commissions and the related national and international organizations to hold the first International Digital Earth Conference in 1999 in Beijing, digital earth theory and technology have attracted widespread attention and made great progress in the world. It is also playing an increasingly important role on promoting sustainable development. The digital earth is a major strategy for China to develop the digital economy. It is also a highly integrated cross-disciplinary field of space science, information science, and earth science. It is an advanced tool for studying the earth quantitatively, understanding the earth deeply, and exploiting the earth scientifically. With the advent of the era of big data, the rapid advancement of artificial intelligence is promoting the further development of the digital earth. Achieving the United Nations Sustainable Development Goals, implementing the “Belt and Road” initiative, and promoting the strategy of Digital China and Beautiful China all require strong support of digital earth and big data technologies.List of Hosts, Co-organizers and Acknowledgement are available in this pdf.

An Overview of Platforms for Big Earth Observation Data Management and Analysis

In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest.

Global multi-scale grid integer coding and spatial indexing: A novel approach for big earth observation data

With the exponentially growing earth observation data of specific sensor-determined resolutions and update frequencies, earth observation has irreversibly arrived in the Big Data era, enabling new insights in science and engineering. With great opportunity comes great challenges regarding efficient and effective data management because earth observation data is of different scales and characterized by complexity in spatial relationships related to the real world. To overcome the challenges is crucial for, for instance, data mining, land surveying, and especially emergency mapping for disaster response. To improve the querying efficiency of big earth observation data, we proposed a novel data management approach: Global Multi-scale Grid Integer Coding and Spatial Indexing. Among our contributions are: (1) proposing Global Multi-scale Grid Integer Coding Model (GMGICM), which presents clustering property in both the scale dimension and spatial dimension, and theoretically facilitates an efficient querying; (2) deliberately applying GMGICM on multi-scale earth observation data spatial indexing, which results in one-dimensional data index, which can be queried using simple B-tree, inversion, and other one-dimensional indexes; (3) designing a strategy to assure the completeness of spatial querying, which is not well solved by existing grid-based coding models. The advantages of our proposed approach have been demonstrated with both simulated and real remote sensing data, with spatial operation 20 times as fast as Geohash and spatial querying 10 times as fast as Oracle Spatial on average. The proposed approach can be easily adapted for three or higher-dimensional earth observation data and bring potential benefit to all big earth observation data analytic projects.

Regional Differences and Influential Factors of Open Public Space in Chinese Cities Based on Big Earth Data

Urban open public spaces that provide multiple services for residents are essential for improving life quality and urban ecosystem function and promoting healthy development, the safety of human settlements and the sustainable development of urban cities. Based on Sustainable Development Goal 11.7 of the United Nations (UN) 2030 Agenda, this study combines the big earth data with the Theil index, a coefficient of variation and Exploratory Spatial Data Analysis (ESDA) to analyze the regional differences and spatial distribution of urban open public space in 2015 for China, and uses the geographical detector to identify key factors that affect the distribution of open public spaces. The results show that (1) open public space scales in provincial-level cities have an ‘East–Central–West’ low-lying land pattern in spatial distribution, where the eastern region has a relatively larger open public space scale. (2) In the prefecture-level cities, the open public space scale increases with an increase in city size and economic development level, and the differences in urban open public space reduce with an increase in city size and increase with a decrease in the economic development level. (3) Factors including economic development level, residents’ living standards, the urbanization level and the population size have sound explanatory powers in varying degrees on the scale of open public spaces; interactions between these factors have improved the explanatory power of the scale of urban open public space.

Big Earth Data science: an information framework for a sustainable planet

ABSTRACT 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.

Indexing Mixed Aperture Icosahedral Hexagonal Discrete Global Grid Systems

Discrete global grid systems (DGGSs) are an emerging multiresolution 3D model used to integrate and analyze big earth data. The characteristic of multiresolution is usually realized by hierarchically subdividing cells on the sphere using certain refinement. This paper introduces mixed aperture three- and four- icosahedral hexagonal DGGSs using two types of refinement, the various combinations of which can provide more resolutions compared with pure aperture hexagonal DGGSs and can flexibly design the aperture sequence according to the target resolutions. A general hierarchy-based indexing method is first designed, and related indexing arithmetics and algorithm are developed based on the indexing method. Then, the grid structure on the surface of the icosahedron is described and by projection spherical grids are obtained. Experiments show that the proposed scheme is superior to pure aperture schemes in choosing grid resolutions and can reduce the data volume by 38.5% in representing 1-km resolution raster dataset; using the proposed indexing arithmetics to replace spherical geometry operations in generating discrete spherical vector lines based on hexagonal cells can improve the generation efficiency.

Big geodata mining: Objective, connotations and research issues

The objective, connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper. Big geodata may be categorized into two domains: big earth observation data and big human behavior data. A description of big geodata includes, in addition to the “5Vs” (volume, velocity, value, variety and veracity), a further five features, that is, granularity, scope, density, skewness and precision. Based on this approach, the essence of mining big geodata includes four aspects. First, flow space, where flow replaces points in traditional space, will become the new presentation form for big human behavior data. Second, the objectives for mining big geodata are the spatial patterns and the spatial relationships. Third, the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data, namely heterogeneity and homogeneity, may change with scale. Fourth, data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships. The big geodata mining methods may be categorized into two types in view of the mining objective, i.e., classification mining and relationship mining. Future research will be faced by a number of issues, including the aggregation and connection of big geodata, the effective evaluation of the mining results and the challenge for mining to reveal “non-trivial” knowledge.

Progress of Earth Observation in China

China is expanding and sharing its capacity for Earth observation by developing sensors, platforms, and launch capabilities in tandem with growing lunar and deep space exploration. China is considering the Moon as a viable Earth observation platform to provide high-quality, planetary-scale data. The platform would produce consistent spatiotemporal data because of its long operational life and the geological stability of the Moon. China is also quickly improving its capabilities in processing and transforming Earth observation data into useful and practical information. Programs such as the Big Earth Data Science Engineering Program (CASEarth) provide opportunities to integrate data and develop "Big Earth Data" platforms to add value to data through analysis and integration. Such programs can offer products and services independently and in collaboration with international partners for data-driven decision support and policy development. With the rapid digital transformation of societies, and consequently increasing demand for big data and associated products, Digital Earth and the Digital Belt and Road Program (DBAR) allow Chinese experts to collaborate with international partners to integrate valuable Earth observation data in regional and global sustainable development.

Progress on CASEarth Satellite Development

CASEarth satellite is the first space Earth science satellite produced by the Chinese Academy of Sciences. The satellite has three payloads:high-definition Thermal Infrared Spectrometer (TIS), high-definition Glimmer Imager for Urbanization (GIU), and high-definition Multispectral Imager for Inshore (MII). These payloads are used to explore the urbanization level and residential layout, the coastal ecosystem, and new methods and approaches of environmental detection during night-time and even under conditions of polar aurora and provide scientific evidence for the refined depiction of human traces. The CASEarth satellite can provide space observation data for A Project on Big Earth Data Science Engineering as well as scientific and application studies inside and outside China.

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.

Use of the McHargian LUSA in agricultural research and decision-making in the age of non-stationarity and big earth observation data

In the past 50 years, there have been two major changes that are of methodological and consequential importance to the McHargian land-use suitability analysis (LUSA): increasing evidence of non-stationarity of global and regional ecological conditions and increasing availability of high-resolution spatial–temporal earth observation data. For 50 years, the McHargian LUSA has been an important analysis tool for designers and planners for both regional conservation planning and development. McHarg’s LUSA is a decision support tool that reduces the dimensions of spatial–temporal data. This makes the technique relevant beyond decision support to spatial identification and prediction of areas of socio-ecological opportunity, risk, and priority. In this article, I use a set of recent studies relating to agricultural LUSA to reveal relationships between the traditional McHargian LUSA and related spatial–temporal research methods that are adapting to more data and non-stationary ecological conditions. Using a classification based on descriptive, predictive, and prescriptive research activities, I organize these related methods and illustrate how linkages between research activities can be used to assimilate more kinds of spatial “big data,” address non-stationarity in socio-ecological systems, and suggest ways to enhance decision-making and collaboration between planners and other sciences.

Data Sharing in CASEarth Project

Big Earth Data Projects (CASEarth; http://www.casearth.com) is a key project to make research data in the fields of biology, ecology, environment and resource science in the Chinese Academy of Sciences (CAS) converge in a synthesis facility, to serve data driving fundamental research and decision making in China. The project was launched January 1, 2018 and funded by CAS. Data sharing is one of most important issues in the project, which is not only a prerequisity, but also a symbol for success in the big data era. A series of regulations refering to the data sharing policy in the CASEarch Project of CAS have been drafted and implemented in the project, including data quality contral, data submitting, data evaluation, data sharing to scientists and decision makers. Dozens of data sets have been re-arranged and submitted to a centralized data warehouse since June 2018, successfully aggregating >250 volumes of detailed monographs of fauna and flora, biodiversity red lists and maps, invasive species data, 3D imagery of specimens, a microbial culture catalogue, genomic data for agricultural/domestic and wild species, checklists and introducing records for botanical gardens, vegetation maps, biodiversity monitoring, nature reserves, wetland birds distribution and conservation, plant phenology (1960-present), ecosystems, paleontological data, and fundamental geographic data such as population, economy, land use, and remote sensing. The Catalogue of Life – China, a species checklist, is the backbone of the biodiversity data sets above. Our data sharing platform (http://data.casearth.cn), launched January 15, 2019, currently houses 5 PB of data. Registration is open to all, and registered users can browse metadata or keyword search on the platform and download data. Although the portal is currently available in Chinese, an English portal will be launched in 2020.

Big Earth data analytics: a survey

ABSTRACTBig Earth data are produced from satellite observations, Internet-of-Things, model simulations, and other sources. The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding, responding, and addressing challenges of Earth sciences and applications. In the past years, new technologies (such as cloud computing, big data and artificial intelligence) have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable. This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain. We first introduce the concepts of big Earth data. The architecture, various functionalities, and supporting modules are then reviewed from a generic methodology aspect. Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools. The driven questions are exemplified through cutting-edge Earth science researches and applications. A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.

Remote Sensing in Environmental Justice Research—A Review

Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people’s physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.

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

ABSTRACTThe 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.

Big Earth Data: a comprehensive analysis of visualization analytics issues

ABSTRACTBig 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.

A remote-sensing image-retrieval model based on an ensemble neural networks

ABSTRACTWith the rapid development of remote-sensing technology and the increasing number of Earth observation satellites, the volume of image datasets is growing exponentially. The management of big Earth data is also becoming increasingly complex and difficult, with the result that it can be hard for users to access the imagery that they are interested in quickly, efficiently and intelligently. To address these challenges, this paper proposes a remote-sensing image-retrieval model based on an ensemble neural networks. This model can make full use of existing training data to improve the efficiency and accuracy of the initial retrieval of remote-sensing images and keep model simple. The retrieval of aerial images using the proposed model is compared with the results obtained using ten individual neural networks and two ensemble neural networks and the results show that the proposed approach has a high degree of precision. In addition, the coverage rate and mean precision show a dramatic improvement of more than 40% compared with existing methods based on normal way. And, the coverage ratio gets 86% for the top 10 return results.

The Digital Belt and Road program in support of regional sustainability

ABSTRACTThe Belt and Road initiative has a significant focus on infrastructure, trade, and economic development across a vast region, and it also provides significant opportunities for sustainable development. The combined pressure of climate variability, intensified use of resources, and the fragility of ecosystems make it very challenging, however, to achieve future sustainability. To develop the path in a sustainable way, it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach. In this context, the Digital Belt and Road (DBAR) program was initiated as an international venture to share expertise, knowledge, technologies, and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development. In this paper, we identify pressing challenges, present the research priorities and foci of the DBAR program, and propose solutions where big Earth data can make significant contributions. This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national, regional and global levels.