Alfaxad/vector-100k

VectorOS Vector 100k SimSat VLM Dataset

VectorOS Vector 100k is a high-fidelity multimodal instruction dataset for fine-tuning vision-language models on population-level vector-borne disease surveillance tasks. It was built for the VectorOS hackathon project and targets LiquidAI/LFM2.5-VL-450M.

The dataset contains 100,000 chat-style examples derived from 10,000 geospatial chips across 30 AOIs. Every accepted chip has a real SimSat Sentinel-2 true-color view, a real SimSat Sentinel-2 NIR-red-green false-color view, a real Mapbox satellite view, and an aligned open-layer evidence overlay.

Created for upload: 2026-05-06T17:28:22Z

What Is Included

Each chip includes:

• image_packets/<aoi>/<chip>_packet.png: a 1024 x 1024 four-panel visual packet.
• sidecars/<aoi>/<chip>_sidecar.json: numeric features, source paths, provenance pointers, quality fields, and license flags.
• targets/<aoi>/<chip>_risk_tile.json: strict VectorOS risk-tile target JSON.
• raw_simsat/<aoi>.tar: per-AOI tar shard containing the raw per-chip SimSat products:
  • sentinel_rgb.png
  • sentinel_false_color_nir_red_green.png
  • sentinel_bands_red_green_blue_nir.npz
  • sentinel_metadata.json
  • mapbox_satellite.png
  • mapbox_metadata.json

The four-panel image packet order is:

1. top-left: SimSat Sentinel-2 true-color RGB
2. top-right: SimSat Sentinel-2 false color NIR-red-green
3. bottom-left: Mapbox satellite context
4. bottom-right: aligned evidence overlay from ESA WorldCover, JRC Global Surface Water, CHIRPS rainfall, WorldPop, OSM, and weak vector/disease labels

Loadable Splits

The records config exposes the VLM instruction records:

Split	Examples
train	72,000
validation	14,000
test	14,000

from datasets import load_dataset

ds = load_dataset("Alfaxad/vector-100k", "records")
print(ds)
print(ds["train"][0]["messages"])

The image, sidecar, and target fields are repository-relative paths. For local fine-tuning, download the repository snapshot and resolve these paths against the snapshot root:

from pathlib import Path
from huggingface_hub import snapshot_download
from datasets import load_dataset

root = Path(snapshot_download(repo_id="Alfaxad/vector-100k", repo_type="dataset"))
ds = load_dataset("Alfaxad/vector-100k", "records")
row = ds["train"][0]
packet_path = root / row["image"]
sidecar_path = root / row["sidecar"]
target_path = root / row["target"]

To extract raw SimSat assets for a specific AOI:

import tarfile
from pathlib import Path
from huggingface_hub import snapshot_download

root = Path(snapshot_download(repo_id="Alfaxad/vector-100k", repo_type="dataset"))
with tarfile.open(root / "raw_simsat" / "bangkok_dengue.tar") as tar:
    tar.extractall(root / "raw_simsat_extracted" / "bangkok_dengue")

Tasks

Each chip contributes 10 task variants:

• risk_tile_json
• officer_explanation
• evidence_cards_json
• uncertainty_audit_json
• field_task_brief
• habitat_patch_summary
• exposure_summary_json
• hard_negative_assessment
• source_provenance_json
• copilot_why_here

Every task is grounded in the same image packet and sidecar. All task variants from a chip stay in the same split.

AOIs And Modules

The dataset covers three initial VectorOS disease modules:

• Dengue/Aedes: Bangkok, Cairns, Colombo, Dhaka, Iquitos, Panama City, Recife, Rio de Janeiro, San Juan, Singapore.
• Malaria/Anopheles: Bobo-Dioulasso, Farafenni, Ifakara, Kisumu, Korhogo, Manhica, Navrongo, Nchelenge, Siaya, Tororo.
• Schistosomiasis: Busia, Jinja, Kafr el-Sheikh, Kafue, Mangochi, Mbita/Homa Bay, Mwanza, Mwea, Niamey, Richard Toll.

Module chip counts:

• dengue/Aedes: 3,334
• malaria/Anopheles: 3,333
• schistosomiasis: 3,333

Sampling Strategy

The chips were stratified to train useful visual and reasoning behavior rather than only label memorization:

• label-positive jitter around GBIF, MAP, or OpenDengue evidence
• hard negatives near water or urban context with no weak labels
• exposure-context chips around populated or operationally relevant areas
• random ecological negatives
• uncertain sparse-context chips

Sample type counts:

• label-positive jitter: 3,524
• hard negative water/urban: 2,762
• exposure context: 1,568
• random ecological: 1,472
• uncertain sparse context: 674

Source Layers

The dataset combines:

• SimSat Sentinel-2 products through Earth Search / Sentinel-2 L2A
• Mapbox satellite static imagery
• CHIRPS v2 daily rainfall
• JRC Global Surface Water v1.4
• ESA WorldCover 2021 v200
• WorldPop population surfaces
• OpenStreetMap / OSM healthcare and operational context
• OpenDengue V1.3 where applicable
• Malaria Atlas Project extracts for malaria AOIs
• GBIF occurrence records for vector or intermediate-host evidence

Validation

Build-time validation:

• chips: 10,000
• total examples: 100,000
• packets exist: 10,000
• Sentinel packets available: 10,000
• Mapbox packets available: 10,000
• target schemas valid: 10,000
• VLM record schemas valid: 100,000

Additional local validation before upload:

• 40,000 referenced images were header/size verified.
• A 400-image random variance spot check found zero low-variance samples.
• The staged package was scanned for temporary HF/Mapbox token leakage before upload.
• The public-release Mapbox flag is user_verified_for_project_use.

Safety And Intended Use

This dataset is intended for fine-tuning and evaluating geospatial VLM behavior for public-health surveillance support. It should be used for population-level, decision-support style reasoning only.

The prompts and targets explicitly avoid:

• individual health claims
• claims of field-verified local disease presence
• individual care or treatment guidance
• calibrated epidemiological forecasting claims

Limitations

• Labels are weak supervision, not ground truth.
• GBIF, MAP, OpenDengue, and other open surveillance records are presence/survey-biased.
• Absence of weak labels is not evidence of field absence.
• Risk scores are instruction-tuning targets generated from open environmental, exposure, and label proxies, not calibrated predictions.
• Mapbox redistribution is marked under the project-specific user verification flag.

Repository Layout

README.md
data/
  train.jsonl
  validation.jsonl
  test.jsonl
image_packets/
  <aoi>/*.png
sidecars/
  <aoi>/*.json
targets/
  <aoi>/*.json
raw_simsat/
  <aoi>.tar
metadata/
  chip_index.json
  chip_index.csv
  splits.json
  manifest.json
  provenance.json
  validation_summary.json
  raw_simsat_index.json
  hf_layout.json
schemas/
  risk_tile_target.schema.json
  vlm_record.schema.json

Citation

If you use this dataset, cite it as:

@dataset{vectoros_vector_100k_simsat,
  title = {VectorOS Vector 100k SimSat VLM Dataset},
  author = {Alfaxad},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Alfaxad/vector-100k}
}