Earth Observations

Advancing the field of earth observation (EO) by leveraging the cutting-edge artificial intelligence (AI) for precise spatial information applicable to disaster mitigation.

Mission

This unit aims to significantly advance the field of EO by employing AI technologies and fostering the collaboration of scientific communities that utilise geo-information for disaster mitigation.

Objectives

To integrate the existing capabilities of EO and Geospatial data analytics, spatial information retrieval and employment of AI for producing applicable solutions in assisting public and private sectors to solve the problem of Australia’s disasters (natural and man-made).

Capabilities

  • Earth observation data analytics for knowledge discovery.
  • Digital earth representation and object recognition.
  • Urban land use and land cover (LULC) modelling to infer for disaster mitigation.
  • EO and AI for Digital Agriculture Services (DAS) to infer crop insurances / disasters.
  • Object retrieval in hierarchy for environmental disaster mitigation.
  • Structural objects and health assessments using IoT sensing to infer disaster reduction.

Impacts

  • DAS to farmers, LULC models to urban planners, structural health to engineers, object characterisation for environmental policy makers, 3d-wind-models for bush-fire applications.
  • Case study examples:

    o  Agriculture field boundary delineation for farmers
    o  Building footprint detection for energy and facility management
    o  Bushfire risk along the roads / highways / airports
    o  Environmental data models for biomass estimation
    o  3d-EO models for topographical sensitivities to disasters
    o  3d-wind models for disaster reductions
    o  Coastal mapping using EO products.

Unit leads