Automated Earth Observation Pipelines: Bridging the Data-to-Decision Gap in Emerging Economies

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@article{1722765,
      author = {Tubolayefa Warekuromor (PhD), Ashibuogwu Kevin},
      title = {Automated Earth Observation Pipelines: Bridging the Data-to-Decision Gap in Emerging Economies},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {62-76},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722765.pdf},
      abstract = {The proliferation of satellite constellations has shifted earth observation (EO) from a data-scarce to a data-surplus ecosystem. However, many emerging economies are struggling to convert vast volumes of captured satellite data into operational decisions, leaving a significant data-to-decision gap. Through a synthesis of peer-reviewed literature and documented operational EO programmes, we examine the transition from legacy download-and-process workflows to cloud-native pipelines integrated with Geospatial Artificial Intelligence (GeoAI) - analysing case studies from Brazil, Australia, and Nigeria, alongside continental and regional initiatives. We find that legal openness of satellite data is necessary but insufficient for operational impact: technical usability and institutional accessibility are equally binding constraints, and institutional integration between EO pipelines and enforcement or planning workflows, as in Brazil's PRODES/DETER system, produces measurable but incomplete gains, reducing illegal clearing without equally slowing broader forest degradation. Cloud-native, data-proximate architectures and automated GeoAI feature extraction reduce the technical burden of the pipeline, while capacity-aware design and clearer institutional governance address the institutional layer. For Nigeria specifically, we discuss how a previously proposed, centralised Sovereign Satellite Data Hub model could serve as one institutional vehicle for operationalising satellite intelligence, pending implementation and independent evaluation.},
      keywords = {Earth observation; data-to-decision gap; cloud-native architectures; geospatial artificial intelligence (GeoAI); emerging economies; satellite data sovereignty},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1722765}
  }