# SkyStream: real fixed-camera sky detection failure modes

Public overview: https://skystream-evaluation.pages.dev/

SkyStream is a first-party, event-triggered optical capture corpus for evaluating
and improving models that watch the sky. Its strongest value is not a “UFO”
label. It is the difficult long tail that causes real systems to alert: small
aircraft and wildlife, insects near the lens, cloud structure, vegetation,
stars, sensor noise, ambiguous motion, and candidate streaks across day,
twilight, and night scenes.

## What is available

- 11,487 linked capture events across 59 active collection days
- 10,483 probe-valid linked H.264 clips totaling 68.8 hours; no audio
- Mostly 4K source video: 7,513 valid clips at 3840×2160 and 3,169 at 2960×1668
- 1,070,975 cleaned detector trajectory rows with normalized boxes, confidence,
  track membership, and relative frame timing
- 2,636 events with human-corrected labels and 3,693 human feedback records
- 73,689 indexed media assets and derivatives in a 441.9 GB source footprint
- 2,340 deduplicated, reviewed benchmark images with 2,700 boxes in COCO and YOLO
  formats; this benchmark is the Forge layer, not the complete corpus

Human event labels include 1,433 aircraft, 588 cloud, 260 sensor/sky noise, 214
meteor candidates, 73 vegetation, 53 insects, and 12 birds. Historical
“meteorite” labels are conservatively renamed `meteor_candidate`; they are not
scientific confirmations.

## Useful buyer outcomes

- Measure false alerts and confidence failures on real small-object sky video.
- Find domain-shift failures across bright sky, clouds, twilight, low light,
  sensor artifacts, foliage, insects, wildlife, and aircraft.
- Fine-tune or mine hard negatives using clips, keyframes, crops, relative-time
  tracks, human corrections, weak-label provenance, and spatial recurrence data.
- Compare candidate models on a frozen reviewed image benchmark.
- Stress-test conventional perspective sky-imager and atmospheric-camera
  pipelines against cloud texture, twilight, near-lens insects, and sensor noise.
- Audit false triggers and image-cleanup logic in optical space-monitoring
  pipelines where terrestrial clutter and camera artifacts are failure modes.

The corpus is especially relevant to bird/bat monitoring, wind-energy wildlife
systems, fixed-camera airspace awareness, optical counter-UAS false-positive
testing, atmospheric imaging, optical space-monitoring cleanup, and general
small-object detection. It contains no verified drone-positive class, so
counter-UAS buyers should treat it as a hard-negative and domain-shift resource,
not a drone-recall benchmark.

This is not an all-sky radiometric or astronomy benchmark. It has no paired
irradiance, cloud fraction, meteorological calibration, hemispheric/fisheye
geometry, telescope, IR, or SWIR data. It also has no verified satellite,
resident-space-object, launch, or meteor ground truth. Candidate streaks are
suitable for clutter triage and false-trigger analysis, not object-recall claims.

## Evaluation path

A small redacted evaluation pack is available with 14 clips, paired
clean/annotated frames, and 128 normalized trajectory points across seven
human-reviewed categories. A paid pilot can then score a buyer model against a
larger stratified subset and deliver an error analysis before either side commits
to a full license.

Public identifiers are pseudonymous. Exact timestamps, exact location, internal
paths, third-party ADS-B/weather/satellite fields, secrets, and embeddings are
withheld. Original media contains a camera timestamp and is handled through
redaction or buyer-specific contractual terms.

Available structures: paid model audit, limited evaluation license, nonexclusive
internal ML license, and custom ongoing collection. Raw-data redistribution and
resale are not included.

Commercial contact: Edmund Olsen, SkyStream — edmundolsen7@icloud.com
