An Operational Analysis Ready Radar Backscatter Dataset for the African Continent

Digital Earth Africa is now providing an operational Sentinel-1 normalized radar backscatter dataset for Africa. This is the first free and open continental scale analysis ready data of this kind that has been developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specification for normalized radar backscatter (NRB) products. Partnership with Sinergise, a European geospatial company and Earth observation data provider, has ensured this dataset is produced efficiently in the cloud infrastructure and can be sustained in the long term. The workflow applies radiometric terrain correction (RTC) to the Sentinel-1 ground range detected (GRD) product, using the Copernicus 30 m digital elevation model (DEM). The method is used to generate data for a range of sites around the world and has been validated as producing good results. This dataset over Africa is made available publicly as a AWS public dataset and can be accessed through the Digital Earth Africa platform and its Open Data Cube API. We expect this dataset to support a wide range of applications, including natural resource monitoring, agriculture, and land cover mapping across Africa.

The Austrian Semantic EO Data Cube Infrastructure

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users can share them in a knowledgebase. We provide insights into our web-based architecture, called Sen2Cube.at, including semantic enrichment, data models, knowledge engineering, semantic querying, and the graphical user interface. Our implemented prototype uses all Sentinel-2 MSI images covering Austria; however, the approach is transferable to other geographical regions and sensors. We demonstrate that explainable, knowledge-based big EO data analysis is possible via graphical semantic querying in EO data cubes.

Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution

The use of optical techniques to interrogate wide ranging samples from semiconductors to biological tissue for rapid analysis and diagnostics has gained wide adoption over the past decades. The desire to collect ever more spatially, spectrally and temporally detailed optical signatures for sample characterization has specifically driven a sharp rise in new optical microscopy technologies. Here we present a high-speed optical scanning microscope capable of capturing time resolved images across 512 spectral and 32 time channels in a single acquisition with the potential for ~0.2 frames per second (256 × 256 image pixels). Each pixel in the resulting images contains a detailed data cube for the study of diverse time resolved light driven phenomena. This is enabled by integration of system control electronics and on-chip processing which overcomes the challenges presented by high data volume and low imaging speed, often bottlenecks in previous systems.

The Swiss data cube, analysis ready data archive using earth observations of Switzerland

Since the opening of Earth Observation (EO) archives (USGS/NASA Landsat and EC/ESA Sentinels), large collections of EO data are freely available, offering scientists new possibilities to better understand and quantify environmental changes. Fully exploiting these satellite EO data will require new approaches for their acquisition, management, distribution, and analysis. Given rapid environmental changes and the emergence of big data, innovative solutions are needed to support policy frameworks and related actions toward sustainable development. Here we present the Swiss Data Cube (SDC), unleashing the information power of Big Earth Data for monitoring the environment, providing Analysis Ready Data over the geographic extent of Switzerland since 1984, which is updated on a daily basis. Based on a cloud-computing platform allowing to access, visualize and analyse optical (Sentinel-2; Landsat 5, 7, 8) and radar (Sentinel-1) imagery, the SDC minimizes the time and knowledge required for environmental analyses, by offering consistent calibrated and spatially co-registered satellite observations. SDC derived analysis ready data supports generation of environmental information, allowing to inform a variety of environmental policies with unprecedented timeliness and quality.

Integrating Open Data Cube and Brazil Data Cube Platforms for Land Use and Cover Classifications

The potential to perform spatiotemporal analysis of the Earth's surface, fostered by a large amount of Earth Observation (EO) open data provided by space agencies, brings new perspectives to create innovative applications. Nevertheless, these big datasets pose some challenges regarding storage and analytical processing capabilities. The organization of these datasets as multidimensional data cubes represents the state-of-the-art in analysis-ready data regarding information extraction. EO data cubes can be defined as a set of time-series images associated with spatially aligned pixels along the temporal dimension. Some key technologies have been developed to take advantage of the data cube power. The Open Data Cube (ODC) framework and the Brazil Data Cube (BDC) platform provide capabilities to access and analyze EO data cubes. This paper introduces two new tools to facilitate the creation of land use and land over (LULC) maps using EO data cubes and Machine Learning techniques, and both built on top of ODC and BDC technologies. The first tool is a module that extends the ODC framework capabilities to lower the barriers to use Machine Learning (ML) algorithms with EO data. The second tool relies on integrating the R package named Satellite Image Time Series (sits) with ODC to enable the use of the data managed by the framework. Finally, water mask classification and LULC mapping applications are presented to demonstrate the processing capabilities of the tools.

INTEGRACuBe: Exploração de dados analíticos em RDF

O crescimento de conjuntos de dados disponíveis na Web que utilizam o padrão RDF propicia análises de dados que envolvem múltiplas dimensões. Segundo a W3C, um dos recursos para analisar dados multidimensionais é a utilização do vocabulário RDF Data Cube. Contudo ainda há uma carência de instrumentos de apoio para aplicação deste vocabulário em conjuntos de dados. Nesse sentido, este artigo propõe o INTEGRACuBe, um ambiente que utiliza um metaesquema e mecanismos semiautomatizados para apoiar o mapeamento de recursos de dados ao metamodelo RDF Data Cube. Como resultado, será possível a exploração de dados analíticos em RDF. Adicionalmente, um estudo de caso é apresentado no cenário de Gerência de Desenvolvimento de Software.

Perseus arm - a new perspective on star formation and spiral structure in our home galaxy

Expansion of (sub)millimetre capabilities to high angular resolution offered with interferometers allows to resolve giant molecular clouds (GMCs) in nearby galaxies. This enables us to place the Milky Way in the context of other galaxies to advance our understanding of star formation in our own Galaxy. We thus remap 12CO (1 - 0) data along the Perseus spiral arm in the outer Milky Way to a fixed physical resolution and present the first spiral arm data cube at a common distance as it would be seen by an observer outside the Milky Way. To achieve this goal we calibrated the longitude-velocity structure of 12CO gas of the outer Perseus arm based on trigonometric distances and maser velocities provided by the BeSSeL survey. The molecular gas data were convolved to the same spatial resolution along the whole spiral arm and regridded on to a linear scale map with the coordinate system transformed to the spiral arm reference frame. We determined the width of the Perseus spiral arm to be 7.8 ± 0.2 km s−1 around the kinematic arm centre. To study the large scale structure we derived the 12CO gas mass surface density distribution of velocities shifted to the kinematic arm centre and arm length. This yields a variation of the gas mass surface density along the arm length and a compression of molecular gas mass at linear scale. We determined a thickness of ∼63 pc on average for the Perseus spiral arm and a centroid of the molecular layer of 8.7 pc.