Space Cubes: Satellite On-Board Processing of Datacube Queries

Datacubes form an accepted cornerstone for analysis- and visualization-ready spatio-temporal data offerings. The increase in user friendliness is achieved by abstracting away from the zillions of files in provider-specific organization. Datacube query languages additionally establish actionable datacubes, enabling users to ask "any query, any time" with zero coding. However, typically datacube deployments are aiming at large scale, data center environments accommodating Big Data and massive parallel processing capabilities for achieving decent performance. In this contribution, we conversely report about a downscaling experiment. In the ORBiDANSE project a datacube engine, rasdaman, has been ported to a cubesat, ESA OPS-SAT, and is operational in space. Effectively, the satellite thereby becomes a datacube service offering the standards-based query capabilities of the OGC Web Coverage Processing (WCPS) geo datacube analytics language. We believe this will pave the way for on-board ad-hoc processing and filtering on Big EO Data, thereby unleashing them to a larger audience and in substantially shorter time

The LANDSUPPORT geospatial decision support system (S-DSS) vision: Operational tools to implement sustainability policies in land planning and management

Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling 'on-the-fly' in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land

MathML-aware Article Conversion from LaTeX

summary:Publishing in Mathematics and theoretical areas in Computer Science and Physics has been predominantly using TeX/LaTeX as a formatting language in the last two decades. This large corpus of born-digital material is both a boon — LaTeX is semi-semantic format where the source often contains indications of the author’s intentions — and a problem — TeX is Turing-complete and authors use this freedom to use thousands of styles and millions of user macros. Several tools have been developed to convert TeX/LaTeX documents to XML-based — i.e. Web and DML-compatible formats. Different DML Projects use different tools, and the selection seems largely accidental. To put the choice of converters for DML projects onto a more solid footing and to encourage competition and feature convergence we survey the market. In this paper we investigate and compare five LaTeX-to-XML transformers in three dimensions: $a$) ergonomic factors like documentation, ease of installation, $b$) coverage, and $c$) quality of the resulting documents (in particular the MathML parts)