{
 "id": "ops-sat-orbitai",
 "entry": 5,
 "name": "OPS-SAT: models trained and run on board",
 "group": "ran",
 "class": "learning",
 "location": "Earth orbit",
 "operator": "European Space Agency",
 "developer": "The OPS-SAT flight control team (Labrèche and colleagues)",
 "host": "OPS-SAT",
 "catalogue": {
  "number": 44878,
  "name": "OPS-SAT",
  "launched": "2019-12-18",
  "decayed": "2024-05-22",
  "match": "by name, the reviewer's judgement"
 },
 "dates": {
  "launched": "2019-12-18",
  "ran": "by April 2021",
  "reported": "2021-04-19",
  "ended": "2024-05-22"
 },
 "hardware": "not stated in what was read",
 "software": "Online machine learning algorithms for training; TensorFlow Lite for inference; k-means for clustering",
 "did": "Trained a fault-detection model in flight from the satellite's own sensor stream, and classified and clustered its own images.",
 "marks": [
  {
   "stage": "L",
   "evidence": 3,
   "basis": [
    "c6"
   ]
  },
  {
   "stage": "R",
   "evidence": 2,
   "basis": [
    "c3",
    "c4",
    "c5"
   ]
  }
 ],
 "claims": [
  {
   "id": "c1",
   "kind": "quote",
   "text": "To our knowledge, this is the first time an artificial intelligence model is trained onboard a flying mission.",
   "speaker": "Georges Labrèche, experimenter",
   "relation": "experimenter",
   "source": "labreche-orbitai",
   "url": "https://georges.fyi/opssat/first-machine-learning-models-trained-in-space/",
   "source_title": "First Machine Learning Models Trained in Space",
   "publisher": "Georges Labrèche (personal site of the experiment's lead)",
   "source_kind": "blog",
   "published": "2021-04-19",
   "published_basis": "page metadata",
   "accessed": "2026-10-04",
   "copy_route": "direct",
   "read": "in full"
  },
  {
   "id": "c2",
   "kind": "quote",
   "text": "One of our models already gave us predictions with a whopping 89% balanced accuracy after only a few runs.",
   "speaker": "Georges Labrèche, experimenter",
   "relation": "experimenter",
   "source": "labreche-orbitai",
   "url": "https://georges.fyi/opssat/first-machine-learning-models-trained-in-space/",
   "source_title": "First Machine Learning Models Trained in Space",
   "publisher": "Georges Labrèche (personal site of the experiment's lead)",
   "source_kind": "blog",
   "published": "2021-04-19",
   "published_basis": "page metadata",
   "accessed": "2026-10-04",
   "copy_route": "direct",
   "read": "in full"
  },
  {
   "id": "c3",
   "kind": "quote",
   "text": "achieving balanced accuracies ranging from 85% to 99% from models trained with the Adagarad RDA, AROW, and NHERD online ML algorithms",
   "speaker": "Labrèche et al., the flight control team",
   "relation": "mission-team",
   "source": "openalex-opssat-abstract",
   "url": "https://doi.org/10.1109/AERO53065.2022.9843402",
   "source_title": "OPS-SAT Spacecraft Autonomy with TensorFlow Lite, Unsupervised Learning, and Online Machine Learning",
   "publisher": "2022 IEEE Aerospace Conference (Labrèche and six co-authors, the flight control team)",
   "source_kind": "abstract",
   "published": "2022-03-05",
   "published_basis": "publication date held by OpenAlex",
   "accessed": "2026-10-04",
   "copy_route": "openalex",
   "read": "abstract in full, as held by OpenAlex; the paper itself could not be reached"
  },
  {
   "id": "c4",
   "kind": "quote",
   "text": "The ability to train models in-flight with data generated on-board without human involvement is an exciting first",
   "speaker": "Labrèche et al., the flight control team",
   "relation": "mission-team",
   "source": "openalex-opssat-abstract",
   "url": "https://doi.org/10.1109/AERO53065.2022.9843402",
   "source_title": "OPS-SAT Spacecraft Autonomy with TensorFlow Lite, Unsupervised Learning, and Online Machine Learning",
   "publisher": "2022 IEEE Aerospace Conference (Labrèche and six co-authors, the flight control team)",
   "source_kind": "abstract",
   "published": "2022-03-05",
   "published_basis": "publication date held by OpenAlex",
   "accessed": "2026-10-04",
   "copy_route": "openalex",
   "read": "abstract in full, as held by OpenAlex; the paper itself could not be reached"
  },
  {
   "id": "c5",
   "kind": "quote",
   "text": "image classification with Convolutional Neural Network (CNN) model inferences using TensorFlow Lite",
   "speaker": "Labrèche et al., the flight control team",
   "relation": "mission-team",
   "source": "openalex-opssat-abstract",
   "url": "https://doi.org/10.1109/AERO53065.2022.9843402",
   "source_title": "OPS-SAT Spacecraft Autonomy with TensorFlow Lite, Unsupervised Learning, and Online Machine Learning",
   "publisher": "2022 IEEE Aerospace Conference (Labrèche and six co-authors, the flight control team)",
   "source_kind": "abstract",
   "published": "2022-03-05",
   "published_basis": "publication date held by OpenAlex",
   "accessed": "2026-10-04",
   "copy_route": "openalex",
   "read": "abstract in full, as held by OpenAlex; the paper itself could not be reached"
  },
  {
   "id": "c6",
   "kind": "data",
   "fields": {
    "OBJECT_NAME": "OPS-SAT",
    "LAUNCH_DATE": "2019-12-18",
    "DECAY_DATE": "2024-05-22"
   },
   "speaker": "CelesTrak satellite catalogue",
   "relation": "catalogue",
   "source": "celestrak-44878",
   "url": "https://celestrak.org/satcat/records.php?CATNR=44878&FORMAT=json",
   "source_title": "Satellite catalogue record 44878",
   "publisher": "CelesTrak",
   "source_kind": "catalogue",
   "published": "undated",
   "published_basis": "a live catalogue record; the access date is the date that matters",
   "accessed": "2026-10-04",
   "copy_route": "json",
   "read": "in full"
  }
 ],
 "conflicts": [],
 "notes": "Evidence 2 rests on the paper's abstract, read in full. The paper's body could not be reached.",
 "changed": "Run raised from R1 to R2 after the paper's abstract was read.",
 "headline": "L3 R2"
}