Datasets:

Alfaxad
/

vector-100k

pretty_name: VectorOS Vector 100k SimSat VLM Dataset
license: other
language:
  - en
task_categories:
  - visual-question-answering
  - text-generation
tags:
  - geospatial
  - remote-sensing
  - public-health
  - vector-borne-disease
  - sentinel-2
  - mapbox
  - multimodal
  - lfm2-vl
  - lfm25-vl
  - weak-supervision
configs:
  - config_name: records
    data_files:
      - split: train
        path: data/train.jsonl
      - split: validation
        path: data/validation.jsonl
      - split: test
        path: data/test.jsonl

VectorOS Vector 100k SimSat VLM Dataset

VectorOS Vector 100k is a high-fidelity multimodal instruction dataset for fine-tuning vision-language models on geospatial epidemiology 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:

top-left: SimSat Sentinel-2 true-color RGB
top-right: SimSat Sentinel-2 false color NIR-red-green
bottom-left: Mapbox satellite context
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 = {Vector 100k SimSat VLM Dataset},
  author = {Alfaxad Eyembe},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Alfaxad/vector-100k}
}