{
  "id": "AIBD-0361",
  "title": "AIMS's ReefScan and ReefCloud apply machine learning to coral reef imagery for automated classification and monitoring; no AI-driven management action documented",
  "summary": "AIMS states ReefScan uses machine learning for real-time object classification and ReefCloud automates benthic image analysis; no reef management decision attributed to the AI was found.",
  "date_observed": "2026-10-20",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "The Australian Institute of Marine Science (AIMS) describes ReefScan as a modular suite of automated marine monitoring systems. It integrates machine learning, imaging sensors and robotics and “uses machine learning for real-time object identification and classification, freeing human effort from routine tasks”. AIMS describes ReefCloud as a platform on which users upload survey images, apply automated benthic analysis and generate consistent outputs. It “reduces manual processing time” and lets teams focus on interpretation and management actions. The ReefCloud.ai site claims accelerated image processing with AI giving results “that would take weeks in hours, with up to 90% accuracy”. AIMS lists a 2022 Asia-Pacific Spatial Excellence Award and a Fast Company honorable mention in the AI and Data category. It lists in-kind support from Accenture and the Allen Coral Atlas. No cited study links AI detection of crown-of-thorns starfish to a Reef Authority control action. AIMS's crown-of-thorns pages describe eDNA detection and chemical-cue control research, not AI. Only the maker's own pages are among the sources.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Automate classification of reef imagery so that monitoring programs report faster and at larger scale. The claimed benefit is the intended one.",
  "system": "AIMS ReefScan (machine-learning object identification on imaging and robotic platforms) and ReefCloud (automated benthic image analysis platform)",
  "model_version": null,
  "developer": "Australian Institute of Marine Science, with in-kind support from Accenture and the Allen Coral Atlas (ReefCloud)",
  "deployer": "AIMS and reef monitoring programs, research institutions and conservation partners using ReefCloud",
  "location": "Great Barrier Reef and other reefs, Australia; ReefCloud users worldwide",
  "domain": "Coral reef monitoring",
  "segment": "Government marine research agency",
  "benefit_type": "Repetitive Task Automation",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "personal",
      "depth": null,
      "reason": "No individual outcome."
    },
    {
      "group": "organizational",
      "depth": null,
      "reason": "Time savings are asserted by AIMS without a quantified case."
    },
    {
      "group": "societal",
      "depth": null,
      "reason": "No reef management action or ecological outcome attributed to the AI."
    }
  ],
  "ai_role": {
    "status": "unassessed",
    "statement": "AIMS's pages state that machine learning performs the image classification in ReefScan and the automated benthic analysis in ReefCloud. That is the claimed source of efficiency. No independent evaluation, named monitoring result or management decision attributable to these systems is among the sources, and only the maker's own account is cited."
  },
  "evidence": [
    {
      "kind": "press release",
      "citation": "Australian Institute of Marine Science (n.d.). ReefScan. aims.gov.au, fetched 20 Oct 2026.",
      "title": "ReefScan",
      "url": "https://www.aims.gov.au/research/technology/reefscan",
      "date": null,
      "written_by": "makers",
      "quote": "ReefScan™ uses machine learning for real-time object identification and classification, freeing human effort from routine tasks and providing more time for knowledge building and decision support."
    },
    {
      "kind": "press release",
      "citation": "Australian Institute of Marine Science (n.d.). ReefCloud. aims.gov.au, fetched 20 Oct 2026.",
      "title": "ReefCloud",
      "url": "https://www.aims.gov.au/research/technology/reefcloud",
      "date": null,
      "written_by": "makers",
      "quote": "This reduces manual processing time and enables teams to focus on interpretation, decision‑making, and management actions rather than data handling."
    },
    {
      "kind": "press release",
      "citation": "ReefCloud.ai (2026). Protecting the future of coral reefs. reefcloud.ai, fetched 20 Oct 2026.",
      "title": "ReefCloud.ai — Protecting the Future of Coral Reefs",
      "url": "https://reefcloud.ai/",
      "date": null,
      "written_by": "makers",
      "quote": "Accelerated image processing with advanced AI. Get results that would take weeks in hours, with up to 90% accuracy."
    }
  ],
  "replication": {
    "produced_by": [
      "Large volumes of standardized reef survey imagery",
      "Machine-learning classifiers for benthic cover and objects",
      "Cloud platform shared by multiple monitoring programs",
      "Institutional partners (Accenture, Allen Coral Atlas)"
    ],
    "must_hold": [
      "Classifier accuracy across reef types and image methods",
      "Continued uploads from monitoring programs",
      "Validation against expert annotation",
      "Links from outputs to management decisions, not yet documented"
    ],
    "echoes": []
  },
  "paired_incident": null,
  "headline": "AIMS's ReefScan and ReefCloud automate reef image analysis",
  "metric": null,
  "comparator": null,
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No later outcome is established as of 20 October 2026.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "Australia",
  "deployer_country": "Australia",
  "unrated_because": null,
  "brief_notes": null,
  "brief": null,
  "revisions": [
    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Benefit type set to Repetitive Task Automation under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/Australian_Institute_of_Marine_Science; https://en.wikipedia.org/wiki/Australian_Institute_of_Marine_Science",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    }
  ],
  "confidence": "claimed",
  "shade": "low",
  "claim_status": "The makers' claim",
  "claim_basis": "Sources: three press releases from the makers. The cited evidence includes no independent source and no finding by a regulator or court.",
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            "account": "The Australian Institute of Marine Science (AIMS) describes ReefScan as a modular suite of automated marine monitoring systems. It integrates machine learning, imaging sensors and robotics and “uses machine learning for real-time object identification and classification, freeing human effort from routine tasks”. AIMS describes ReefCloud as a platform on which users upload survey images, apply automated benthic analysis and generate consistent outputs. It “reduces manual processing time” and lets teams focus on interpretation and management actions. The ReefCloud.ai site claims accelerated image processing with AI giving results “that would take weeks in hours, with up to 90% accuracy”. AIMS lists a 2022 Asia-Pacific Spatial Excellence Award and a Fast Company honorable mention in the AI and Data category. It lists in-kind support from Accenture and the Allen Coral Atlas. No cited study links AI detection of crown-of-thorns starfish to a Reef Authority control action. AIMS's crown-of-thorns pages describe eDNA detection and chemical-cue control research, not AI. Only the maker's own pages are among the sources.",
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        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
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        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
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          }
        },
        "provenance": {
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          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
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            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
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        "state": "value_present",
        "value": {
          "kind": "record_fields",
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            "location": "Great Barrier Reef and other reefs, Australia; ReefCloud users worldwide",
            "developer_country": "Australia",
            "deployer_country": "Australia"
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        },
        "provenance": {
          "kind": "legacy_record",
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            "purpose": "Automate classification of reef imagery so that monitoring programs report faster and at larger scale. The claimed benefit is the intended one."
          }
        },
        "provenance": {
          "kind": "legacy_record",
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
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      },
      "record_dates": {
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          "kind": "record_fields",
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              },
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                "date": "2026-10-21",
                "change": "Benefit type set to Repetitive Task Automation under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
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                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
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              },
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                "editor": "AIBD"
              },
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                "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.",
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        "review_state": "record_mapped",
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      "replication.attempts[]": {
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        "coverage": "partial"
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        "value": {
          "kind": "record_fields",
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            "replication": {
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                "Large volumes of standardized reef survey imagery",
                "Machine-learning classifiers for benthic cover and objects",
                "Cloud platform shared by multiple monitoring programs",
                "Institutional partners (Accenture, Allen Coral Atlas)"
              ],
              "must_hold": [
                "Classifier accuracy across reef types and image methods",
                "Continued uploads from monitoring programs",
                "Validation against expert annotation",
                "Links from outputs to management decisions, not yet documented"
              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
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          "record_fields": [
            "replication"
          ],
          "source_refs": []
        },
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        "coverage": "partial"
      },
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        "value": {
          "kind": "record_fields",
          "fields": {
            "revisions": [
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                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
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              },
              {
                "date": "2026-10-21",
                "change": "Benefit type set to Repetitive Task Automation under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
                "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Australian_Institute_of_Marine_Science; https://en.wikipedia.org/wiki/Australian_Institute_of_Marine_Science",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sector": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "domain": "Coral reef monitoring",
            "segment": "Government marine research agency"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].access_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].citation_locator": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
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                "kind": "press release",
                "citation": "Australian Institute of Marine Science (n.d.). ReefScan. aims.gov.au, fetched 20 Oct 2026.",
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                "url": "https://www.aims.gov.au/research/technology/reefscan",
                "written_by": "makers",
                "quote": "ReefScan™ uses machine learning for real-time object identification and classification, freeing human effort from routine tasks and providing more time for knowledge building and decision support."
              },
              {
                "kind": "press release",
                "citation": "Australian Institute of Marine Science (n.d.). ReefCloud. aims.gov.au, fetched 20 Oct 2026.",
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                "url": "https://www.aims.gov.au/research/technology/reefcloud",
                "written_by": "makers",
                "quote": "This reduces manual processing time and enables teams to focus on interpretation, decision‑making, and management actions rather than data handling."
              },
              {
                "kind": "press release",
                "citation": "ReefCloud.ai (2026). Protecting the future of coral reefs. reefcloud.ai, fetched 20 Oct 2026.",
                "title": "ReefCloud.ai — Protecting the Future of Coral Reefs",
                "url": "https://reefcloud.ai/",
                "written_by": "makers",
                "quote": "Accelerated image processing with advanced AI. Get results that would take weeks in hours, with up to 90% accuracy."
              }
            ]
          }
        },
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        },
        "review_state": "record_mapped",
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      },
      "sources[].id": {
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            "AIBD-0361-source-0001",
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            "AIBD-0361-source-0003"
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          "role": "Existing evidence items, in their original order"
        },
        "provenance": {
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          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
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        "review_state": "not_assessed",
        "coverage": "partial"
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        "state": "value_present",
        "value": {
          "kind": "record_fields",
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                "kind": "press release",
                "citation": "Australian Institute of Marine Science (n.d.). ReefScan. aims.gov.au, fetched 20 Oct 2026.",
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              },
              {
                "kind": "press release",
                "citation": "Australian Institute of Marine Science (n.d.). ReefCloud. aims.gov.au, fetched 20 Oct 2026.",
                "title": "ReefCloud",
                "url": "https://www.aims.gov.au/research/technology/reefcloud",
                "written_by": "makers",
                "quote": "This reduces manual processing time and enables teams to focus on interpretation, decision‑making, and management actions rather than data handling."
              },
              {
                "kind": "press release",
                "citation": "ReefCloud.ai (2026). Protecting the future of coral reefs. reefcloud.ai, fetched 20 Oct 2026.",
                "title": "ReefCloud.ai — Protecting the Future of Coral Reefs",
                "url": "https://reefcloud.ai/",
                "written_by": "makers",
                "quote": "Accelerated image processing with advanced AI. Get results that would take weeks in hours, with up to 90% accuracy."
              }
            ]
          }
        },
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].upstream_sources": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "strategic_assessments[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "strategic assessments"
      },
      "summary": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "summary": "AIMS states ReefScan uses machine learning for real-time object classification and ReefCloud automates benthic image analysis; no reef management decision attributed to the AI was found.",
            "account": "The Australian Institute of Marine Science (AIMS) describes ReefScan as a modular suite of automated marine monitoring systems. It integrates machine learning, imaging sensors and robotics and “uses machine learning for real-time object identification and classification, freeing human effort from routine tasks”. AIMS describes ReefCloud as a platform on which users upload survey images, apply automated benthic analysis and generate consistent outputs. It “reduces manual processing time” and lets teams focus on interpretation and management actions. The ReefCloud.ai site claims accelerated image processing with AI giving results “that would take weeks in hours, with up to 90% accuracy”. AIMS lists a 2022 Asia-Pacific Spatial Excellence Award and a Fast Company honorable mention in the AI and Data category. It lists in-kind support from Accenture and the Allen Coral Atlas. No cited study links AI detection of crown-of-thorns starfish to a Reef Authority control action. AIMS's crown-of-thorns pages describe eDNA detection and chemical-cue control research, not AI. Only the maker's own pages are among the sources."
          }
        },
        "provenance": {
          "kind": "legacy_record",
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            "account"
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          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].autonomy_permissions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].component_role": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].deployment_configuration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].inputs_outputs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_name": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].product_name": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "system": "AIMS ReefScan (machine-learning object identification on imaging and robotic platforms) and ReefCloud (automated benthic image analysis platform)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].provider": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "application_developer": "Australian Institute of Marine Science, with in-kind support from Accenture and the Allen Coral Atlas (ReefCloud)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Mapped from the existing case description without a new external source review.",
          "record_fields": [
            "developer"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "taxonomy_crosswalks[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "title": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "title": "AIMS's ReefScan and ReefCloud apply machine learning to coral reef imagery for automated classification and monitoring; no AI-driven management action documented",
            "headline": "AIMS's ReefScan and ReefCloud automate reef image analysis"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "title",
            "headline"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "version": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
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          ],
          "role": "SHA-256 of normalized original case data"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Deterministic content version assigned by the local migration.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "workflow": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "The Australian Institute of Marine Science (AIMS) describes ReefScan as a modular suite of automated marine monitoring systems. It integrates machine learning, imaging sensors and robotics and “uses machine learning for real-time object identification and classification, freeing human effort from routine tasks”. AIMS describes ReefCloud as a platform on which users upload survey images, apply automated benthic analysis and generate consistent outputs. It “reduces manual processing time” and lets teams focus on interpretation and management actions. The ReefCloud.ai site claims accelerated image processing with AI giving results “that would take weeks in hours, with up to 90% accuracy”. AIMS lists a 2022 Asia-Pacific Spatial Excellence Award and a Fast Company honorable mention in the AI and Data category. It lists in-kind support from Accenture and the Allen Coral Atlas. No cited study links AI detection of crown-of-thorns starfish to a Reef Authority control action. AIMS's crown-of-thorns pages describe eDNA detection and chemical-cue control research, not AI. Only the maker's own pages are among the sources.",
            "replication": {
              "produced_by": [
                "Large volumes of standardized reef survey imagery",
                "Machine-learning classifiers for benthic cover and objects",
                "Cloud platform shared by multiple monitoring programs",
                "Institutional partners (Accenture, Allen Coral Atlas)"
              ],
              "must_hold": [
                "Classifier accuracy across reef types and image methods",
                "Continued uploads from monitoring programs",
                "Validation against expert annotation",
                "Links from outputs to management decisions, not yet documented"
              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 26,
      "reviewed": 0,
      "record_mapped": 26,
      "not_assessed": 65,
      "complete": false,
      "by_state": {
        "value_present": 26,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 65,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}