--- 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` ![dataset_montage](https://cdn-uploads.huggingface.co/production/uploads/6375af60e3413701a9f01c0f/ZktzLpUrpBiI5clDaf1ah.png) ## What Is Included Each chip includes: - `image_packets//_packet.png`: a 1024 x 1024 four-panel visual packet. - `sidecars//_sidecar.json`: numeric features, source paths, provenance pointers, quality fields, and license flags. - `targets//_risk_tile.json`: strict VectorOS risk-tile target JSON. - `raw_simsat/.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 | ```python 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: ```python 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: ```python 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 ```text README.md data/ train.jsonl validation.jsonl test.jsonl image_packets/ /*.png sidecars/ /*.json targets/ /*.json raw_simsat/ .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: ```bibtex @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} } ```