ChrisRPL/satellite-civilian-conflict-disruption-reporter-v1

Satellite Civilian Conflict Disruption Reporter v1

Dataset ID: ChrisRPL/satellite-civilian-conflict-disruption-reporter-v1

Status

This is a valid diagnostic reporter-schema dataset, not the current Blackline Atlas canonical model gate. The canonical compact calibration/gold dataset remains ChrisRPL/satellite-disruption-triage-aux-v2-2.

Use this dataset for future schema-simplification experiments only after respecting the mixed source licenses. Do not treat the associated diagnostic LoRA ChrisRPL/lfm25-vl-civilian-conflict-reporter-lora-v1 as an accepted or HF-Jobs-completed model.

This is a compact, quality-first paired-image dataset for fine-tuning and evaluating a vision-language model as a civilian disruption reporter. It compares a baseline satellite image and a current satellite image, uses location/date/context text, and asks the assistant to return concise strict JSON about visible civilian disruption evidence.

The task is deliberately narrower than general disaster detection and narrower than policy-action prediction. It does not train tactical targeting, military asset detection, route intelligence, or military ranking. The final recommended_action is derived from visible evidence and uncertainty.

Output schema

Assistant responses are strict JSON with exactly these keys:

{
  "visible_change_summary": "string",
  "civilian_disruption_evidence": ["collapsed_building | roof_loss | burn_scar | ..."],
  "negative_evidence": ["no_visible_change | low_visibility | sar_speckle_or_modality_artifact | ..."],
  "uncertainty_factors": ["string"],
  "severity_hint": "none | low | medium | high",
  "recommended_action": "discard | defer | downlink_now",
  "confidence": 0.0,
  "short_rationale": "string"
}

Files

• train_flat.jsonl, eval_flat.jsonl, calibration_flat.jsonl
• train_sft.jsonl, eval_sft.jsonl, calibration_sft.jsonl
• compatibility aliases: eval_calibration_flat.jsonl, eval_calibration_sft.jsonl
• images/baseline/*.png, images/current/*.png
• metadata.json, validation_report.md, source_audit.md

Counts

Split	Rows
train	121
eval	79
calibration	6
total	206

Recommended-action balance

{
  "calibration": {"discard": 1, "downlink_now": 5},
  "eval": {"defer": 21, "discard": 20, "downlink_now": 38},
  "train": {"defer": 30, "discard": 33, "downlink_now": 58}
}

Modality balance

{"optical-to-SAR": 144, "optical-to-optical": 60, "optical-to-optical-cloudy": 2}

Source balance

{
  "Blackline Atlas Sentinel-2 paired capture": 15,
  "GabeT29/BRIGHT-XView2Format via ChrisRPL/satellite-disruption-triage-aux-v2-2": 144,
  "xBD-Ukraine local materialization from sda-kr/xbd-ukraine lineage": 47
}

License table

Source/license	Rows	Notes
CC-BY-NC-4.0	144	Non-commercial restriction applies
Copernicus Sentinel data terms / internal derived capture; verify for downstream redistribution	15	Respect upstream source terms
MIT	47	Respect upstream source terms

Composite dataset license is cc-by-nc-4.0 because BRIGHT-derived rows are CC-BY-NC-4.0. Users must also respect per-source terms, especially Sentinel/Copernicus attribution and xBD-Ukraine lineage.

Split policy

• BRIGHT explosion events are event-held-out: Bata appears in train; Beirut appears in eval.
• xBD-Ukraine rows are location-held-out by city: Mariupol train, Rubizhne eval, Marinka calibration.
• Blackline Sentinel-2 captures are case/event-held-out; no capture case is in more than one split.
• Image-pair SHA-256 checks prevent exact image-pair duplication across splits.

Known limitations

• This is a compact diagnostic dataset, not a comprehensive global conflict-damage benchmark.
• Some labels are inherited or rule-derived from BRIGHT/xBD annotations and Blackline scenario labels; they are not all expert human VLM annotations.
• BRIGHT rows are optical-to-SAR and non-commercial (CC-BY-NC-4.0), so the composite dataset is non-commercial.
• Sentinel-2 rows are macro-scale and may miss small roof-level damage; use uncertainty fields and analyst review.
• The dataset intentionally excludes military asset detection, route intelligence, targeting, and tactical ranking tasks.

Intended use

Research and demo-supporting analyst assistance for humanitarian/civilian-infrastructure disruption triage. Outputs are evidence summaries, not operational commands. Human review is required before any external reporting.