Goldeneye project results and impact on mining

Marko Savolainen, Kamen Bogdanov, Jari Havisto, Francisco Gutierres, Taras Matselyukh, Marko Paavola, Andreas Knobloch

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


Improved use of Earth observation with sensor and positioning data can offer significant exploitation and environmental control benefits and increase the productivity of mines. The EU-funded Goldeneye project has developed a GoldenAI platform, which can fuse high-resolution data from satellites, drones, and in-situ sensors to generate new high-resolution data from an entire mine. This data is being processed and converted into actionable intelligence with new tools developed in the project, offering improvements to exploration, mine safety, environmental monitoring, exploitation, and increasing extraction in pilot mines. The project has combined remote sensing and positioning technologies and taken advantage of Earth observation and Earth GNSS data with data fusion and processing powered by data analytics and machine learning algorithms. The GoldenAI platform and other developed technologies have been piloted in five trial mine sites across Europe and the results are presented in this paper.
Original languageEnglish
Title of host publicationEU SuperCluster Lapland Geoconference – October 30–31, 2023, Hotel Santa Claus, Rovaniemi, Finland
Subtitle of host publicationAbstracts
EditorsVesa Nykänen, Nick Cook, Juha Kaija
PublisherGeological Survey of Finland
Number of pages5
Publication statusPublished - 2023
MoE publication typeNot Eligible
EventEuropean Union SuperCluster Lapland Geoconference - Rovaniemi, Finland
Duration: 30 Oct 202331 Oct 2023

Publication series

SeriesOpen File Research Report


ConferenceEuropean Union SuperCluster Lapland Geoconference


The Goldeneye H2020 project is funded by the European Union under agreement number 869398.


  • Goldeneye
  • Mining
  • Earth observation
  • Active hyperspectral spectroscopy
  • Time-gated Raman spectroscopy
  • Platform


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