Earth observations data cubes (EODCs) are a paradigm transforming the way users interact with large spatio-temporal Earth observation (EO) data. It enhances connections between data, applications and users facilitating management, access and use of analysis ready data (ARD). The ambition is allowing users to harness big EO data at a minimum cost and effort. This significant interest is illustrated by various implementations that exist. The novelty of the approach results in different innovative solutions and the lack of commonly agreed definition of EODC. Consequently, their interoperability has been recognized as a major challenge for the global change and Earth system science domains. The objective of this paper is preventing EODC from becoming silos of information; to present how interoperability can be enabled using widely-adopted geospatial standards; and to contribute to the debate of enhanced interoperability of EODC. We demonstrate how standards can be used, profiled and enriched to pave the way to increased interoperability of EODC and can help delivering and leveraging the power of EO data building, efficient discovery, access and processing services.

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Paving the Way to Increased Interoperability of Earth Observations Data Cubes

Urban planning support based on the photovoltaic potential of buildings: a multi-scenario ranking system

As geospatial data is increasingly massive and complex, large data volume and rich data source query retrieval are among the urgent issues in need of resolution. Spatial indices are widely used to organize data and optimize queries. However, tree-based indices are increasingly difficult to adapt to a high-efficiency query, and the combination of a grid index and space-filling curve can help decrease the dimensions to improve the query efficiency, but can also lead to data redundancy as one object can cover several grids. To solve the aforementioned problems, this paper proposes a method to manage and query data using a grid-code-array spatial index based on a GeoSOT global subdivision model. For the first time, a grid code was organized in a code-array format and an inverted index was constructed on the column of the code-array. By adding a grid-code-array data structure, we verified the feasibility and efficiency and compared the R-tree index in the Oracle Spatial system and the grid index in the ArcSDE geodatabase for Oracle, which are the most widely used. Experimental results showed that the spatial index we proposed has obvious advantages, which could solve the problem of storage redundancy and query results, and effectively improve spatial queries particularly when the data volume is large.

Method for managing and querying geo-spatial data using a grid-code-array spatial index

Over the past few decades, geoportals have been considered as the key technological solutions for easy access to Earth observation (EO) products, and the implementation of spatial data infrastructure (SDI). However, less attention has been paid to developing an efficient model for crowdsourcing EO products through geoportals. To this end, a new model called the “Open Community-Based Crowdsourcing Geoportal for Earth Observation Products” (OCCGEOP) was proposed in this study. The model was developed based on the concepts of volunteered geographic information (VGI) and community-based geoportals using the latest open technological solutions. The key contribution lies in the conceptualization of the frameworks for automated publishing of standard map services such as the Web Map Service (WMS) and the Web Coverage Service (WCS) from heterogeneous EO products prepared by volunteers as well as the communication portion to request voluntary publication of the map services and giving feedback for quality assessment and assurance. To evaluate the feasibility and performance of the proposed model, a prototype implementation was carried out by conducting a pilot study in Iran. The results showed that the OCCGEOP is compatible with the priorities of the new generations of geoportals, having some unique features and promising performance.

Open Community-Based Crowdsourcing Geoportal for Earth Observation Products: A Model Design and Prototype Implementation

Environmental scientific research is highly becoming data-driven and dependent on high performance computing infrastructures to process ever increasing large volume and diverse data sets. Consequently, there is a growing recognition of the need to share data, methods, algorithms, and infrastructure to make scientific research more effective, efficient, open, transparent, reproducible, accessible, and usable by different users. However, Earth Observations (EO) Open Science is still undervalued, and different challenges remains to achieve the vision of transforming EO data into actionable knowledge by lowering the entry barrier to massive-use Big Earth Data analysis and derived information products. Currently, FAIR-compliant digital repositories cannot fully satisfy the needs of EO users, while Spatial Data Infrastructures (SDI) are not fully FAIR-compliant and have difficulties in handling Big Earth Data. In response to these issues and the need to strengthen Open and Reproducible EO science, this paper presents SwissEnvEO, a Spatial Data Infrastructure complemented with digital repository capabilities to facilitate the publication of Ready to Use information products, at national scale, derived from satellite EO data available in an EO Data Cube in full compliance with FAIR principles.

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SwissEnvEO: A FAIR National Environmental Data Repository for Earth Observation Open Science

Scientists as well public institutions dealing with geospatial data often work with a large amount of heterogeneous data deriving from different sources. Without a well-defined, organized structure they face problems in finding and reusing existing data, and as consequence this may cause data inconsistency and storage problems. A catalog system based on the metadata of spatial data facilitates the management of large amount of data and offers service to retrieve, discover and exchange geographic data in an quick and easy fashion. Currently, most online catalogs are more focusing on the geographic data and there has been only few interests in catalogizing Earth observation data, in which in addition the acquisition information matters. This article presents an automatic metadata extraction approach that creates from different optical data deriving from various satellite missions of scientific interest (i.e. MODIS, LANDSAT, RapidEye, Suomi-NPP VIIRS, Sentinel-1A, Sentinel-2A) metadata information, based on an extended model of the standard ISO 19115. The XML schema ISO 19139-2 with the support of gridded and imagery information defined in ISO 19115-2 was examined, and based on the requirements of experts working in the research field of Earth observation the schema was extended. The XML schema ISO 19139-2 and its extension has been deployed as a new schema plugin in the spatial catalog Geonetwork Open Source in order to store all relevant metadata information about satellite data and the appropriate acquisition and processing information in an online catalog. A real-world scenario that is productively used in the EURAC research group institute for Applied Remote Sensing illustrates a workflow management for Earth observation data including data processing, metadata extraction, generation and distribution.

https://link.springer.com/content/pdf/10.1007%2Fs12145-016-0276-0.pdf

Organizing earth observation data inside a spatial data infrastructure

When it comes to land cover classification, the process of deriving the land classes is complex due to possible errors in algorithms, spatio-temporal heterogeneity of the Earth observation data, variation in availability and quality of reference data, or a combination of these. This article proposes a probabilistic graphical model approach, in the form of a cluster graph, to boost geospatial classifications and produce a more accurate and robust classification and uncertainty product. Cluster graphs can be characterized as a means of reasoning about geospatial data such as land cover classifications by considering the effects of spatial distribution, and inter-class dependencies in a computationally efficient manner. To assess the capabilities of our proposed cluster graph boosting approach, we apply it to the field of land cover classification. We make use of existing land cover products (GlobeLand30, CORINE Land Cover) along with data from Volunteered Geographic Information (VGI), namely OpenStreetMap (OSM), to generate a boosted land cover classification and the respective uncertainty estimates. Our approach combines qualitative and quantitative components through the application of our probabilistic graphical model and subjective expert judgments. Evaluating our approach on a test region in Garmisch-Partenkirchen, Germany, our approach was able to boost the overall land cover classification accuracy by 1.4% when compared to an independent reference land cover dataset. Our approach was shown to be robust and was able to produce a diverse, feasible and spatially consistent land cover classification in areas of incomplete and conflicting evidence. On an independent validation scene, we demonstrated that our cluster graph boosting approach was generalizable even when initialized with poor prior assumptions.

A Cluster Graph Approach to Land Cover Classification Boosting

Visual Analytics-enabled Bayesian Network Approach to Reasoning about Public Camera Data.

. This research aims to investigate the role of visualization of the user generated data that can empower the geoportal of GlobeLand30 produced by NGCC (National Geomatics Center of China). The focus is set on the development of a concept of tools that can extend the Geo-tagging functionality and make use of it for different target groups. The anticipated tools should improve the continuous data validation, updating and efficient use of the remotely-sensed data distributed within GlobeLand30.

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Visual tools for crowdsourcing data validation within the globeland30 geoportal

. This article demonstrates the ability of the Bayesian Network analysis for the recognition of uncertainty patterns associated with the fusion of various land cover data sets including GlobeLand30, CORINE (CLC2006, Germany) and land cover data derived from Volunteered Geographic Information (VGI) such as Open Street Map (OSM). The results of recognition are expressed as probability and uncertainty maps which can be regarded as a by-product of the GlobeLand30 data. The uncertainty information may guide the quality improvement of GlobeLand30 by involving the ground truth data, information with superior quality, the know-how of experts and the crowd intelligence. Such an endeavor aims to pave a way towards a seamless validation of global land cover data on the one hand and a targeted knowledge discovery in areas with higher uncertainty values on the other hand.

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Ekaterina Chuprikova

Papers

Organizing earth observation data inside a spatial data infrastructure

A Cluster Graph Approach to Land Cover Classification Boosting

Visual Analytics-enabled Bayesian Network Approach to Reasoning about Public Camera Data.

Visual tools for crowdsourcing data validation within the globeland30 geoportal

Towards Seamless Validation of Land Cover Data