{"id":"AIBD-1135","title":"EPRI reports that Pano AI's camera platform detected 85% of test fires within 10 minutes of visible smoke in a 2021 pilot with PGE and PG&E","summary":"EPRI reports that in a 2021 utility pilot, Pano AI's platform detected 60% of test fires within 4 minutes of visible smoke and 85% within 10 minutes.","date_observed":"2021-01-01","date_basis":"event","date_precision":"year","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"In 2021, Portland General Electric and Pacific Gas & Electric hosted pilots of Pano AI's Rapid Detect platform through Incubatenergy Labs, an EPRI program with utility sponsors. EPRI was the technical advisor. Cameras on mountaintop towers turn 360 degrees every minute, and AI-based methods detect smoke. The Pano Intelligence Center applies machine learning across all stations to confirm sightings before alerts go out. PGE had two stations and PG&E had access to 13. EPRI's report of March 2022 says that the final November model detected 60% of test vegetation fires within 4 minutes of visible smoke and 85% within 10 minutes. The false positive rate fell from about 1.6% in late July to 0.37% in early October as the models improved. Across more than 50 alerts to utilities, the alerting accuracy rate was above 90%. In the Round Fire in northern California, Pano AI alerted before the fire was reported to first responders. The report gives no count or location for the test fires. It gives no comparison with other ways of detecting fires.","category":"aggregate result","unexpected":false,"purpose":"Pano AI built Rapid Detect to find wildfire smoke early with rotating mountaintop cameras and AI and to alert utilities and fire agencies.","system":"Pano AI Rapid Detect platform (Pano Stations with AI-based smoke detection, and the Pano Intelligence Center)","model_version":"final November production model","developer":"Pano AI (San Francisco, California)","deployer":"Portland General Electric (two Pano Stations) and Pacific Gas & Electric (13 stations), host utilities of the 2021 pilot","location":"Service territories of Portland General Electric in Oregon and Pacific Gas & Electric in California, United States","domain":"Wildfire detection / electric utilities","segment":null,"benefit_type":"Improved Task Accuracy","work_activities":["4.A.1.a.2","4.A.1.b.1"],"taxonomy_note":"The pilot measured how fast and how accurately the platform detected fires. Its effect on wildfire risk is not measured.","registry_ids":{},"ratings":[{"group":"organizational","depth":2,"reason":"The platform detected 85% of test fires within 10 minutes of visible smoke, and its alerting accuracy rate across more than 50 alerts to the utilities was above 90%."},{"group":"societal","depth":null,"reason":"The report names one fire that the platform flagged before it was reported to first responders but gives no outcome for that fire or any other."}],"ai_role":{"status":"unassessed","statement":"The report credits AI-based models with detecting smoke and says that their learning cut the false positive rate during the pilot. It does not compare detection with and without the models. The Pano Intelligence Center confirmed each sighting before alerts went out, and the report does not separate that review from the models."},"evidence":[{"kind":"evaluation report","citation":"EPRI (Electric Power Research Institute). Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness. Product 3002023034, March 2022. EPRI was the technical advisor to pilots hosted by Portland General Electric and Pacific Gas & Electric (evaluator's account).","title":"Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness","url":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","date":"2022-03-01","written_by":"evaluator","quote":"Pano AI's platform was able to detect 60% of the test fires within 4 minutes from the time smoke was first visible and 85% within 10 minutes"},{"kind":"evaluation report","citation":"EPRI (Electric Power Research Institute). Incubatenergy Labs 2021 Pilot Project Report, March 2022, Results & Learnings (false positives).","title":"Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness","url":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","date":"2022-03-01","written_by":"evaluator","quote":"Learning by AI-based models reduced the false positive rate during the pilot project, helping utility analysts focus attention on actual incidents posing potential wildfire risks."}],"replication":{"produced_by":["Cameras on existing mountaintop towers turned 360 degrees every minute and sent ultra-high-definition images.","AI-based methods detected smoke, and the Pano Intelligence Center applied machine learning across all stations to confirm sightings before alerts.","Analysts used triangulation and maps of utility infrastructure to decide which incidents to escalate."],"must_hold":["Stations need towers with power, connectivity and permits, about 10 miles apart for triangulation.","Watch out to about 25 miles needs clear days."],"echoes":[]},"paired_incident":null,"headline":"*Pano AI* spots most test fires within minutes in a utility pilot","metric":"60% of test fires within 4 minutes of visible smoke","comparator":{"against":"The platform's false positive rate earlier in the pilot","before":"about 1.6% in late July","after":"0.37% in early October","basis":"measured","quote":"As shown in the figure, the false positive rate was about 1.6% in late July, declining to 0.37% in early October.","url":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product"},"later":null,"value":null,"developer_country":"United States","deployer_country":"United States","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-11-01","change":"Added the record from the finding of EPRI.","source":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","editor":"AIBD"}],"confidence":"claimed","shade":"low","claim_status":"The evaluator's account","claim_basis":"Sources: the evaluators' evaluation report. The cited evidence includes no independent source and no finding by an external body.","case_jurisdictions":["United States"],"profile":{"case_id":"AIBD-1135","catalog_version":"1.0.0","source_fingerprint":"cc4e35393e3c18ec7f2e5c8cb798f16c8f3964b02eb48d7542b65990ec383882","collection_refs":[{"collection_id":"aibd","version":"2026-10-31"},{"collection_id":"querist","version":"2026-10-25"}],"source_ids":["AIBD-1135-source-0001","AIBD-1135-source-0002"],"families":{"strategic assessments":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]},"evaluations":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]},"monetary observations":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]},"institutional decisions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"Editorial assignment.","review_state":"not_assessed","coverage":"partial","record_ids":[]}},"fields":{"actors[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"developer":"Pano AI (San Francisco, California)","deployer":"Portland General Electric (two Pano Stations) and Pacific Gas & Electric (13 stations), host utilities of the 2021 pilot"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["developer","deployer"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"actors[].ownership_control":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"ai_contribution":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"ai_relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"category":"aggregate result","ai_role":{"status":"unassessed","statement":"The report credits AI-based models with detecting smoke and says that their learning cut the false positive rate during the pilot. It does not compare detection with and without the models. The Pano Intelligence Center confirmed each sighting before alerts went out, and the report does not separate that review from the models."}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ai_role","category"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"assessments[].alternatives":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].assessor_date":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].depth":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The platform detected 85% of test fires within 10 minutes of visible smoke, and its alerting accuracy rate across more than 50 alerts to the utilities was above 90%."},{"group":"societal","reason":"The report names one fire that the platform flagged before it was reported to first responders but gives no outcome for that fire or any other."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ratings"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"assessments[].display_confidence":{"state":"value_present","value":{"kind":"structured","data":{"confidence":"low","evidence_word":"claimed","claim_status":"The evaluator's account","basis":"Sources: the evaluators' evaluation report. The cited evidence includes no independent source and no finding by an external body.","interpretation":"Existing display rule preserved; the richer outcome-specific confidence question still needs review."}},"provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"assessments[].rationale":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The platform detected 85% of test fires within 10 minutes of visible smoke, and its alerting accuracy rate across more than 50 alerts to the utilities was above 90%."},{"group":"societal","reason":"The report names one fire that the platform flagged before it was reported to first responders but gives no outcome for that fire or any other."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ratings"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"assessments[].rubric_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].status_basis":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"assessments[].target":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The platform detected 85% of test fires within 10 minutes of visible smoke, and its alerting accuracy rate across more than 50 alerts to the utilities was above 90%."},{"group":"societal","reason":"The report names one fire that the platform flagged before it was reported to first responders but gives no outcome for that fire or any other."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ratings"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"availability":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"brief":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"case_kind":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"case_links[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"claims[].evidence_links":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"claims[].evidence_strength":{"state":"value_present","value":{"kind":"structured","data":{"answer":"The evaluator's account. Sources: the evaluators' evaluation report. The cited evidence includes no independent source and no finding by an external body.","evidence_word":"claimed","claim_status":"The evaluator's account"}},"provenance":{"kind":"source_review","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":["AIBD-1135-source-0001","AIBD-1135-source-0002"],"reviewed_on":"2026-10-29"},"review_state":"reviewed","coverage":"complete","display":{"answer":"The evaluator's account. Sources: the evaluators' evaluation report. The cited evidence includes no independent source and no finding by an external body.","inferred":false}},"claims[].missingness":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"claims[].proposition":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"cross_border_flows[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"decisions[].action_outcome":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"decisions[].grounds":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"decisions[].institution_task":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"decisions[].success_criterion":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"institutional decisions"},"deployment_stage":{"state":"value_present","value":{"kind":"structured","data":{"classification":"pilot","answer":"The system was used in a pilot. PGE and PG&E ran the platform as pilots through the end of the 2021 fire season.","scope":"The period and activity described in the case."}},"provenance":{"kind":"source_review","basis":"Editorial assignment.","record_fields":["account","evidence"],"source_refs":["AIBD-1135-source-0001"],"reviewed_on":"2026-10-29"},"review_state":"reviewed","coverage":"complete","display":{"answer":"The system was used in a pilot. PGE and PG&E ran the platform as pilots through the end of the 2021 fire season.","inferred":false}},"economics[].amount":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].costs[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].counterfactual":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].currency_price_basis":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].gross_net":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].measure_type":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].period_unit":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].realization":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].recipient_payer":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].return_payback":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"economics[].valuation_method":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"monetary observations"},"evaluations[].ai_uplift":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].comparator":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].design":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].effect":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].exposure":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].id":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].measurement_method":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].metric":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].reproduction_materials":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].setting_sample":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"evaluations[].uncertainty_limitations":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"evaluations"},"external_references[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"follow_up[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"human_oversight":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"id":{"state":"value_present","value":{"kind":"record_fields","fields":{"id":"AIBD-1135"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["id"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"infrastructure_dependencies[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].beneficiaries":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].beneficiary_count":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].benefit_type":{"state":"value_present","value":{"kind":"record_fields","fields":{"benefit_type":"Improved Task Accuracy","taxonomy_note":"The pilot measured how fast and how accurately the platform detected fires. Its effect on wildfire risk is not measured."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["benefit_type","taxonomy_note"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].claim":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"In 2021, Portland General Electric and Pacific Gas & Electric hosted pilots of Pano AI's Rapid Detect platform through Incubatenergy Labs, an EPRI program with utility sponsors. EPRI was the technical advisor. Cameras on mountaintop towers turn 360 degrees every minute, and AI-based methods detect smoke. The Pano Intelligence Center applies machine learning across all stations to confirm sightings before alerts go out. PGE had two stations and PG&E had access to 13. EPRI's report of March 2022 says that the final November model detected 60% of test vegetation fires within 4 minutes of visible smoke and 85% within 10 minutes. The false positive rate fell from about 1.6% in late July to 0.37% in early October as the models improved. Across more than 50 alerts to utilities, the alerting accuracy rate was above 90%. In the Round Fire in northern California, Pano AI alerted before the fire was reported to first responders. The report gives no count or location for the test fires. It gives no comparison with other ways of detecting fires.","benefit_type":"Improved Task Accuracy"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","benefit_type"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].distribution":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].duration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].id":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].stakeholder_group":{"state":"value_present","value":{"kind":"record_fields","fields":{"ratings":[{"group":"organizational","depth":2,"reason":"The platform detected 85% of test fires within 10 minutes of visible smoke, and its alerting accuracy rate across more than 50 alerts to the utilities was above 90%."},{"group":"societal","reason":"The report names one fire that the platform flagged before it was reported to first responders but gives no outcome for that fire or any other."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["ratings"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].status":{"state":"value_present","value":{"kind":"record_fields","fields":{"benefit_status":"reported"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["benefit_status"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"outcomes[].tradeoffs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"outcomes[].unexpected":{"state":"value_present","value":{"kind":"record_fields","fields":{"unexpected":false,"purpose":"Pano AI built Rapid Detect to find wildfire smoke early with rotating mountaintop cameras and AI and to alert utilities and fire agencies."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["unexpected","purpose"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"places[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"location":"Service territories of Portland General Electric in Oregon and Pacific Gas & Electric in California, United States","developer_country":"United States","deployer_country":"United States"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["location","developer_country","deployer_country"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"purpose":{"state":"value_present","value":{"kind":"record_fields","fields":{"purpose":"Pano AI built Rapid Detect to find wildfire smoke early with rotating mountaintop cameras and AI and to alert utilities and fire agencies."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["purpose"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"record_dates":{"state":"value_present","value":{"kind":"record_fields","fields":{"date_observed":"2021-01-01","date_basis":"event","date_precision":"year","revisions":[{"date":"2026-11-01","change":"Added the record from the finding of EPRI.","source":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","editor":"AIBD"}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["date_observed","date_basis","date_precision","revisions"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"replication.attempts[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"replication.conditions":{"state":"value_present","value":{"kind":"record_fields","fields":{"replication":{"produced_by":["Cameras on existing mountaintop towers turned 360 degrees every minute and sent ultra-high-definition images.","AI-based methods detected smoke, and the Pano Intelligence Center applied machine learning across all stations to confirm sightings before alerts.","Analysts used triangulation and maps of utility infrastructure to decide which incidents to escalate."],"must_hold":["Stations need towers with power, connectivity and permits, about 10 miles apart for triangulation.","Watch out to about 25 miles needs clear days."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"revision_history[]":{"state":"value_present","value":{"kind":"record_fields","fields":{"revisions":[{"date":"2026-11-01","change":"Added the record from the finding of EPRI.","source":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","editor":"AIBD"}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["revisions"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sector":{"state":"value_present","value":{"kind":"record_fields","fields":{"domain":"Wildfire detection / electric utilities"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["domain"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].access_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"sources[].citation_locator":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"evaluation report","citation":"EPRI (Electric Power Research Institute). Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness. Product 3002023034, March 2022. EPRI was the technical advisor to pilots hosted by Portland General Electric and Pacific Gas & Electric (evaluator's account).","title":"Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness","url":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","date":"2022-03-01","written_by":"evaluator","quote":"Pano AI's platform was able to detect 60% of the test fires within 4 minutes from the time smoke was first visible and 85% within 10 minutes"},{"kind":"evaluation report","citation":"EPRI (Electric Power Research Institute). Incubatenergy Labs 2021 Pilot Project Report, March 2022, Results & Learnings (false positives).","title":"Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness","url":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","date":"2022-03-01","written_by":"evaluator","quote":"Learning by AI-based models reduced the false positive rate during the pilot project, helping utility analysts focus attention on actual incidents posing potential wildfire risks."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].id":{"state":"value_present","value":{"kind":"identifiers","identifiers":["AIBD-1135-source-0001","AIBD-1135-source-0002"],"role":"Existing evidence items, in their original order"},"provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"sources[].language":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"sources[].relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"evaluation report","citation":"EPRI (Electric Power Research Institute). Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness. Product 3002023034, March 2022. EPRI was the technical advisor to pilots hosted by Portland General Electric and Pacific Gas & Electric (evaluator's account).","title":"Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness","url":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","date":"2022-03-01","written_by":"evaluator","quote":"Pano AI's platform was able to detect 60% of the test fires within 4 minutes from the time smoke was first visible and 85% within 10 minutes"},{"kind":"evaluation report","citation":"EPRI (Electric Power Research Institute). Incubatenergy Labs 2021 Pilot Project Report, March 2022, Results & Learnings (false positives).","title":"Incubatenergy Labs 2021 Pilot Project Report: 360-Degree, AI-Based Imaging for Wildfire Situational and Locational Awareness","url":"https://restservice.epri.com/publicdownload/000000003002023034/0/Product","date":"2022-03-01","written_by":"evaluator","quote":"Learning by AI-based models reduced the false positive rate during the pilot project, helping utility analysts focus attention on actual incidents posing potential wildfire risks."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].upstream_sources":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"strategic_assessments[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"strategic assessments"},"summary":{"state":"value_present","value":{"kind":"record_fields","fields":{"summary":"EPRI reports that in a 2021 utility pilot, Pano AI's platform detected 60% of test fires within 4 minutes of visible smoke and 85% within 10 minutes.","account":"In 2021, Portland General Electric and Pacific Gas & Electric hosted pilots of Pano AI's Rapid Detect platform through Incubatenergy Labs, an EPRI program with utility sponsors. EPRI was the technical advisor. Cameras on mountaintop towers turn 360 degrees every minute, and AI-based methods detect smoke. The Pano Intelligence Center applies machine learning across all stations to confirm sightings before alerts go out. PGE had two stations and PG&E had access to 13. EPRI's report of March 2022 says that the final November model detected 60% of test vegetation fires within 4 minutes of visible smoke and 85% within 10 minutes. The false positive rate fell from about 1.6% in late July to 0.37% in early October as the models improved. Across more than 50 alerts to utilities, the alerting accuracy rate was above 90%. In the Round Fire in northern California, Pano AI alerted before the fire was reported to first responders. The report gives no count or location for the test fires. It gives no comparison with other ways of detecting fires."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["summary","account"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].autonomy_permissions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].component_role":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].deployment_configuration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].inputs_outputs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_name":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_version":{"state":"value_present","value":{"kind":"record_fields","fields":{"model_version":"final November production model"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["model_version"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].product_name":{"state":"value_present","value":{"kind":"record_fields","fields":{"system":"Pano AI Rapid Detect platform (Pano Stations with AI-based smoke detection, and the Pano Intelligence Center)"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["system"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].provider":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"taxonomy_crosswalks[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"title":{"state":"value_present","value":{"kind":"record_fields","fields":{"title":"EPRI reports that Pano AI's camera platform detected 85% of test fires within 10 minutes of visible smoke in a 2021 pilot with PGE and PG&E","headline":"*Pano AI* spots most test fires within minutes in a utility pilot"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["title","headline"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"version":{"state":"value_present","value":{"kind":"identifiers","identifiers":["0a88591ff8920de9334e9d02a060f96480c935b52a59dffaa298701fcae46a15"],"role":"SHA-256 of the complete public record and its assessments"},"provenance":{"kind":"curatorial","basis":"SHA-256 of the public record, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"workflow":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"In 2021, Portland General Electric and Pacific Gas & Electric hosted pilots of Pano AI's Rapid Detect platform through Incubatenergy Labs, an EPRI program with utility sponsors. EPRI was the technical advisor. Cameras on mountaintop towers turn 360 degrees every minute, and AI-based methods detect smoke. The Pano Intelligence Center applies machine learning across all stations to confirm sightings before alerts go out. PGE had two stations and PG&E had access to 13. EPRI's report of March 2022 says that the final November model detected 60% of test vegetation fires within 4 minutes of visible smoke and 85% within 10 minutes. The false positive rate fell from about 1.6% in late July to 0.37% in early October as the models improved. Across more than 50 alerts to utilities, the alerting accuracy rate was above 90%. In the Round Fire in northern California, Pano AI alerted before the fire was reported to first responders. The report gives no count or location for the test fires. It gives no comparison with other ways of detecting fires.","replication":{"produced_by":["Cameras on existing mountaintop towers turned 360 degrees every minute and sent ultra-high-definition images.","AI-based methods detected smoke, and the Pano Intelligence Center applied machine learning across all stations to confirm sightings before alerts.","Analysts used triangulation and maps of utility infrastructure to decide which incidents to escalate."],"must_hold":["Stations need towers with power, connectivity and permits, about 10 miles apart for triangulation.","Watch out to about 25 miles needs clear days."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":29,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":27,"review_not_assessed":62,"not_assessed":0,"complete":false,"by_state":{"value_present":29,"not_reported":0,"unresolved":62,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"0ec0fe382af4c1ba97b604f4d1f07d70b0ae433e696aff0eb355ab41a24c2dff"}