satellite-disruption-triage-aux-v1-3

Civilian Conflict-Disruption Satellite VLM Dataset — Auxiliary / v1.3

This is an auxiliary dataset for training and evaluating Vision-Language Models (VLMs) to perform civilian conflict-disruption triage from paired satellite imagery. It is not a tactical intelligence dataset and not a canonical expert benchmark.

Scope & Purpose

The target task is detecting macro-scale civilian infrastructure disruption caused by war, armed conflict, bombardment, shelling, explosions, siege, or major unrest. The model compares a baseline (pre-event) satellite image with a current (post-event) image and outputs a structured triage decision.

Included civilian infrastructure:

• Hospitals, schools, residential/civilian building clusters
• Food/logistics warehouses, grain silos, markets, aid hubs
• Ports, bridges, water facilities, power/desalination plants
• IDP camps, public infrastructure

Explicitly excluded:

• Military bases, weapons systems, troop positions, air defenses
• Tactical route intelligence, target ranking, strike planning data

Dataset Version

• Version: 1.3.0
• Total examples: 3,332
• Conflict-core examples: 3,272 (2,127 train / 1,145 eval)
• Non-conflict auxiliary examples: 60 (42 train / 18 eval) — capped at <2% of total
• Image resolution: 512×512 PNG
• License: CC-BY-NC-4.0 (Maxar Open Data, non-commercial)
• Build date: 2026-04-25

Schema

Flat JSONL (train_flat.jsonl, eval_flat.jsonl)

Each row:

{
  "example_id": "string",
  "baseline_image": "images/baseline/...png",
  "current_image": "images/current/...png",
  "target_output": {
    "action": "discard | defer | downlink_now",
    "category": "conflict_building_damage | conflict_hospital_damage | conflict_food_logistics_damage | conflict_water_infrastructure_damage | conflict_bridge_or_access_damage | conflict_port_or_silo_damage | conflict_urban_area_damage | explosion_damage | no_visible_disruption | ambiguous_or_low_visibility | other_conflict_civilian_disruption",
    "rationale": "short sentence",
    "bbox_norm": [x_min, y_min, x_max, y_max] | null
  },
  "source_dataset": "string",
  "source_event": "string",
  "source_image_name": "string",
  "provenance": "source URL or dataset reference",
  "modality": "optical-to-optical | optical-to-SAR | SAR-to-SAR | other",
  "location_name": "string",
  "country": "string",
  "conflict_context": "short string",
  "baseline_date": "YYYY-MM-DD or null",
  "current_date": "YYYY-MM-DD or null",
  "license": "string",
  "label_method": "mask-derived | metadata-derived | manual-review | vlm-assisted | weak-label",
  "damage_ratio": float | null,
  "destruction_ratio": float | null,
  "subset": "conflict_core | non_conflict_auxiliary"
}

SFT JSONL (train_sft.jsonl, eval_sft.jsonl)

Conversational format for VLM fine-tuning:

{
  "example_id": "string",
  "images": ["images/baseline/...png", "images/current/...png"],
  "messages": [
    {"role": "system", "content": "You are a civilian conflict-disruption satellite triage model. Return strict JSON only."},
    {"role": "user", "content": "Compare the baseline and current satellite images. Focus only on macro-scale civilian disruption caused by conflict or explosion. Return action, category, rationale, and bbox_norm."},
    {"role": "assistant", "content": "{ strict JSON target_output }"}
  ],
  "source_dataset": "string",
  "provenance": "string"
}

Action Definitions

• downlink_now — Clear macro-visible civilian disruption from conflict/explosion (buildings destroyed, widespread damage)
• defer — Plausible but ambiguous conflict disruption (partial damage, smoke, low visibility, SAR/optical mismatch)
• discard — No visible disruption, weak evidence, non-civilian target, or invalid pair

Split Policy

Event-held-out evaluation. No event family, city damage campaign, or near-duplicate tile appears in both train and eval.

• Ukraine: Split by city/district (18 cities → train, 9 cities → eval)
• Beirut explosion: Exclusively in eval
• Bata explosion: Exclusively in train
• Auxiliary natural disasters: Events split across train/eval, no overlap

Action Distribution

Split	downlink_now	defer	discard
Train (conflict core)	558 (26.2%)	1,100 (51.7%)	469 (22.1%)
Eval (conflict core)	330 (28.8%)	539 (47.1%)	276 (24.1%)
Target	45%	20%	35%

Note: The target balance was not fully achievable because the BRIGHT dataset genuinely contains more moderate-damage tiles (defer) than catastrophic-damage tiles (downlink_now). Labels are honest and derived from pixel-level damage masks, not artificially balanced.

Event / Country Coverage

Country	Cities/Areas	Examples	Split
Ukraine	27 cities (Kharkiv, Mariupol, Bucha, Irpin, Bakhmut, etc.)	2,844	Train+Eval
Lebanon	Beirut Port	230	Eval only
Equatorial Guinea	Bata	198	Train only
USA	Various (wildfires, hurricanes)	60	Train+Eval (aux)

Data Sources

Source	License	Rows	Status
BRIGHT (Kullervo/BRIGHT)	CC-BY-NC-4.0	3,272 conflict	Used
xBD (DIUx/xView2)	CC-BY-NC-4.0	60 auxiliary	Used
UNOSAT Gaza damage assessments	Proprietary	—	Excluded — no bulk download
UNOSAT Ukraine damage layers	Proprietary	—	Excluded — no bulk download
PRS/ETH Ukraine Zenodo	CC-BY-4.0	—	Excluded — empty/missing files
Maxar Open Data	CC-BY-NC-4.0	—	Excluded — raw imagery, no ML-ready labels

Full source audit in source_audit.md.

Known Limitations

1. Ukraine-only armed conflict examples: The only public, redistributable conflict-damage satellite dataset with paired pre/post images and pixel labels is BRIGHT Ukraine. Gaza, Syria, Yemen, Sudan, Myanmar, Nagorno-Karabakh, Iraq, Libya, Iran, and Mexico cartel-conflict areas have no public ML-ready satellite damage benchmarks.

2. Optical-to-SAR modality gap: ~54% of conflict examples use pre-event optical + post-event SAR. Radar speckle and geometry differences can create false-change signals.

3. No per-building type labels: BRIGHT provides building damage masks but not building-type classification (hospital vs residential vs warehouse). All high-damage tiles are labeled conflict_building_damage.

4. Template-generated rationales: Rationale text is auto-generated from damage thresholds, not human expert review.

5. No exact dates: BRIGHT provides only event names, not acquisition dates.

6. Non-commercial license: CC-BY-NC-4.0 restricts commercial use.

How to Use

from datasets import load_dataset

dataset = load_dataset("imagefolder", data_dir="satellite-disruption-triage-aux-v1-3")

Or load the JSONL directly for VLM training:

import json

train = [json.loads(l) for l in open("train_flat.jsonl")]

Citation

@dataset{satellite_disruption_triage_v1_3,
  title = {Satellite Disruption Triage Auxiliary Dataset v1.3},
  author = {ChrisRPL},
  year = {2026},
  url = {https://huggingface.co/datasets/ChrisRPL/satellite-disruption-triage-aux-v1-3}
}

Contact

For issues or contributions, open a discussion on the Hugging Face Hub.