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Use cases

  • Spatial and temporal assessment of neighbourhood building stock

    Partners: Stiftelsen Norsk Institutt for Luftforskning, Norwegian Institute for Air Research

    The purpose of this use case is to focus on the potential impacts of building stocks on the material use and greenhouse gas emissions over time. This use case is geared towards two of the EGD priority actions (i.e., climate change and circular economy) emphasizing the need to enhance energy and resource efficiency in the building construction sectors, specifically during the renovation activities.

    Research question:

    • Are enough data available to have a spatial estimation of building materials availability and energy performance of buildings?
    • To what extent can datacube infrastructure support actors, researcher, stakeholders, etc. to tackle the Green Deal priority action plans related to climate change and circular economy?
    • How efficiently and effectively can datacube infrastructure be scaled up to cover building stock at regional or national level?
    • Can datacube infrastructure knowledge be easily transferred and reproduced for other types of infrastructure (i.e., green, blue, and grey infrastructure)?

  • Biodiversity occurrence cubes

    Partner: Naturhistorisches Museum Wien, Natural History Museum Vienna (NHMW)

    One core concern of biodiversity research pertains to gaining a better understanding of the multitude of factors influencing whether species flourish or wither depending on their local environment. To date, this research was painstaking, with researchers manually gathering data on potentially relevant factors before they can commence their analyses. In this use case, we aim to leverage the diverse resources becoming available in spatiotemporal grids to widen the scope of potential factors that can be correlated with species distribution; these identified correlations must then be vetted by domain experts to identify underpinning causalities before being utilized at a wider scope for predictions. In addition, available point-based occurrence data stemming from both specimens and observations will be transformed to multidimensional gridded formats for integration and analysis with other FAIRiCUBE holdings.

  • Biodiversity and agriculture nexus

    Partners: Wageningen Environmental Research

    This use case focuses on biodiversity as one of the European Green Deal (EGD) priority actions, while considering the agricultural landscape as focus environment for the investigation of impact of activities at farm field level on the biodiversity. To describe a basic conceptual design of biodiversity assessment within this study we use the Dutch Biodiversity Monitor (DBM), that measures the effect on biodiversity resulting from impact that farming has on the physical conditions on the environment expressed by Key Performance Indicators (KPIs).

    Research questions:
    • Can the integration and ML-based analysis of currently available biodiversity, agriculture, environmental, and remote sensing data provide comprehensive, verifiable, and actionable insights for different regions?
    • Can datacube functionality and ML help in finding patterns between effects of farm level measures, indicators of physical conditions and direct measures of biodiversity?
    • Can the insights obtained in the study region be extended to other regions, learned patterns reused by applying transfer learning?
  • Urban adaptation to climate change

    Partners: space4environment and Stiftelsen Norsk Institutt for Luftforskning, Norwegian Institute for Air Research

    This use case covers the priority “climate change” with a specific focus on the adaptation of cities to climate change. The use case would also be able to link up with other priority actions, i.e., “zero pollution” and “biodiversity”. Even the action items “deforestation” and “compliance assurance” might have a link.

    Research questions:
    • Do the currently available European data help cities in being appropriately informed about climate change and its impact on cities?
    • Can big data (historical, real-time, and modelled forecast spatial data) and ML approaches help European cities to prepare for the impacts of climate change and take adaptive measures/make informed decisions?
    • In how far can datacubes enable local, regional, national, and European decision-makers to achieve the goals of the European Green Deal?
    • Does the European Green Deal data space provide the best possible means to collect, store and provide European data on climate change impacts on cities?