Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Evidence shows how disasters caused by extreme weather events are surely becoming more frequent at all latitudes, and are definitely representing an ever-growing risk in the floods- and landslides-prone territory of Burundi. The still present inequalities of its society and the vulnerable economy make this African country a very relevant and particularly complex case for hazards risk management. A fundamental step in this task is to correctly map the so-called exposure elements to risk, which most notably comprise the population and households, the critical infrastructure, and the transportation network. In this article, we assess the quality of the OpenStreetMap (OSM) roads database in Burundi at national level, with a perspective in its fitness-for-use in the assistance for disaster risk mitigation purposes, which were adopted in the context of a recent multi-hazards risk assessment and mapping exercise on behalf of the International Organisation for Migration. Despite not touching all aspects of volunteered geographic information data quality that are commonly found in literature for a comprehensive assessment, we believe the information provided in this study can be of help for stakeholders in their decision to use the OSM dataset in an otherwise data-scarce area.

The use of large multidimensional, complex datasets to study the roots and the consequences of real-world phenomena is systematically gaining importance in the recent years. These datasets and their requisite metadata can be managed by query able databases, and the increasing processing capabilities of distributed computing and Array DBMS are opening new scenarios for Web-based access and analysis. The spatio-temporal nature of these observations requires the establishment of an automated machine-readable system that seamlessly accounts for time as not just an attribute, but rather as one more axis in the n-dimensional structure. This paper proposes a conceptual framework for the integration of temporal with spatial dimensions. Based on the widely used Geography Markup Language (GML) it exploits temporal Coordinate Reference Systems (CRSs) in analogy to, and interoperable with, well-known spatial CRSs. The proposed approach is then concretely applied to the OGC Web protocols Web Coverage Service (WCS) and Web Coverage Processing Service (WCPS), by means of which a user can visualize, aggregate, process remotely stored archives of scientific data. It is based on an open-source CRS resolver, SECORE, which translates URL identifiers of CRSs into their GML definitions. SECORE has been adopted by OGC as its standard CRS resolver, and the whole concept is supported by an ad-hoc OGC working group.

The continuous growth of remotely sensed data raises the need for efficient ways of accessing data archives. The classical model of accessing remote sensing (satellite) archives via distribution of large files is increasingly making way for a more dynamic and interactive data service. A challenge, though, is interoperability of such services, in particular when multi-dimensional data and advanced processing are involved. Individually crafted service interfaces typically do not allow substitution and combination of services. Open standards can provide a way forward if they are powerful enough to address both data and processing model. The OGC Web Coverage Service (WCS) is a modular service suite which provides high-level interface definitions for data access, subsetting, filtering, and processing of spatio-temporal raster data. WCS based service interfaces to data archives deliver data in their original semantics useful for further client-side processing, as opposed to the Web Map Service (WMS) (de la Beaujardière, 2006) which performs a pre-rendering into images only useful for display to humans. In this paper we present a case study where the OGC coverage data and service model defines the client/server interface for a climate data service. In particular, we show how flexible temporal analysis can be performed efficiently on massive spatio-temporal coverage objects. This service, which is operational on a several Terabyte data holding, has been established as part of the EarthServer initiative focusing on Big Data in the Earth and Planetary sciences.