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README.md

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+---
+license: mit
+task_categories:
+  - image-classification
+  - visual-question-answering
+language:
+  - en
+size_categories:
+  - n<1K
+tags:
+  - satellite-imagery
+  - earth-observation
+  - orbital-triage
+  - vlm
+  - lfm2
+  - liquid-ai
+pretty_name: ORION Satellite Triage Dataset
+---
+
+# Dataset
+
+The ORION dataset is a curated collection of satellite imagery and triage labels used to fine-tune the VLM for orbital image classification. Images are fetched from SimSat's Mapbox API and paired with classification prompts and ground-truth labels.
+
+## Dataset Structure
+
+```
+images/
+  low_ocean_pacific_nemo.png
+  med_city_chicago.png
+  high_port_rotterdam.png
+  ...
+train_dataset.jsonl
+val_dataset.jsonl
+test_dataset.jsonl
+```
+
+- **images/**: 512x512 RGB satellite images fetched from SimSat
+- **train_dataset.jsonl**: Training samples (240 targets x 2 coordinate dropout = 480 records)
+- **val_dataset.jsonl**: Validation samples (60 targets, always with coordinates; used for eval_loss + best checkpoint selection)
+- **test_dataset.jsonl**: Test samples (60 targets, always with coordinates; held out for ablation and evaluation)
+
+## Target Definitions
+
+360 target locations organized by triage priority and visual morphology:
+
+| Class  | Count | Visual Morphology                                                                                  |
+| ------ | ----- | -------------------------------------------------------------------------------------------------- |
+| LOW    | 120   | Featureless natural terrain: oceans, deserts, ice sheets, dense canopy, geological formations      |
+| MEDIUM | 120   | Standard human civilization: urban grids, suburban sprawl, agriculture, towns, infrastructure      |
+| HIGH   | 120   | Strategic anomalies: mega-ports, mega-airports, energy/dams, mega-mines, military/space facilities |
+
+### LOW Morphologies
+
+1. Standard voids: oceans and water bodies
+2. Standard voids: deserts, ice sheets, dense canopy
+3. Hard LOW: coastlines and boundaries that resemble artificial structures
+4. Hard LOW: geological anomalies (craters, calderas) that mimic mines
+5. Hard LOW: fractals and textures (deltas, salt flats, reefs) that mimic city streets
+
+### MEDIUM Morphologies
+
+1. Urban grids: dense city centers worldwide
+2. Suburban sprawl: low-density residential areas
+3. Agriculture: crop fields, pivot irrigation, terracing
+4. Standard infrastructure: regional airports, rail yards, commercial zones
+5. Towns and settlements: isolated clusters in varied terrain
+
+### HIGH Morphologies
+
+1. Mega-ports: Rotterdam, Singapore, LA, etc.
+2. Mega-airports: Atlanta, Denver, Dubai, Daxing, etc.
+3. Energy and dams: nuclear plants, solar farms, hydroelectric dams
+4. Mega-mines and extreme industrial: open-pit mines, refineries
+5. Space, military, and chokepoints: launch pads, naval bases, canals
+
+## Generation Process
+
+[`data_gen.py`](https://Saransh-cpp.github.io/ORION/guides/data-gen/) generates the dataset by:
+
+1. **Proximity filter**: removes targets closer than 2 km to each other (Haversine distance) to avoid duplicate imagery
+2. **Shuffle and split**: deterministic 3-way IID split (`random.seed(42)`): 240 train / 60 val / 60 test
+3. **Image fetch**: for each target, fetches a 512x512 satellite image from SimSat's Mapbox static image API at `GET http://localhost:9005/data/image/mapbox`
+4. **JSONL generation**: creates conversation-format records for fine-tuning
+
+### JSONL Record Format
+
+Each line in the JSONL files is a JSON object:
+
+```json
+{
+  "image": "orion_dataset/images/high_port_rotterdam.png",
+  "conversations": [
+    {
+      "role": "user",
+      "content": "<image>\nYou are an autonomous orbital triage assistant..."
+    },
+    {
+      "role": "assistant",
+      "content": "{\"reason\": \"Extreme-density geometric cargo terminals...\", \"category\": \"HIGH\"}"
+    }
+  ]
+}
+```
+
+## Coordinate Dropout Augmentation
+
+For training samples, each target produces two JSONL records: one with GPS coordinates in the prompt and one without. This 50% coordinate dropout teaches the model to classify based on visual features alone, making it robust when GPS data is noisy or unavailable.
+
+## Prompt Template
+
+The prompt matches the ChatML format used by the fine-tuned model and the on-board VlmInferenceEngine:
+
+```
+You are an autonomous orbital triage assistant. Analyze this high-resolution RGB
+satellite image captured at Longitude: X, Latitude: Y.
+Strictly use one of these categories based on visual morphology:
+- HIGH: ...
+- MEDIUM: ...
+- LOW: ...
+You MUST output your response as a valid JSON object. To ensure accurate visual
+reasoning, you must output the "reason" key FIRST, followed by the "category" key.
+```
+
+## Expected Model Output
+
+```json
+{
+  "reason": "Extreme-density geometric cargo terminals and massive vessel berthing.",
+  "category": "HIGH"
+}
+```
+
+## Related
+
+- [Fine-tuned model](https://huggingface.co/Saransh-cpp/orion-qlora-lfm2.5-vl-1.6b)
+- [Training guide](https://Saransh-cpp.github.io/ORION/guides/training/)
+- [Training pipeline](https://Saransh-cpp.github.io/ORION/ground-segment/training/)
+- [Data flow architecture](https://Saransh-cpp.github.io/ORION/architecture/data-flow/)