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Datasets:
Tonic
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nutonic-sft-init-upload-test

Tasks:
Image-Text-to-Text
Modalities:
Image
Text
Geospatial
Formats:
json
Languages:
English
Size:
< 1K
Tags:
satellite
land-cover
lfm-vl
geospatial
Libraries:
Datasets
pandas
Polars
License:
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nutonic-sft-init-upload-test / README.md
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metadata 
license: apache-2.0
task_categories:
  - image-text-to-text
language:
  - en
tags:
  - satellite
  - land-cover
  - lfm-vl
  - geospatial
pretty_name: NU-TONIC raw SFT init (satellite imagery + land-cover labels)

NU-TONIC raw SFT init

Satellite imagery and aligned land-cover outputs packaged as image–text rows for fine-tuning LFM-VL (leap-finetune vlm_sft format). JSONL user prompts name the modality (satellite imagery vs. overhead context) where it matters.

Provenance

  • Locations: GeoGuessr-style POIs (default HF source: stochastic/random_streetview_images_pano_v0.0.2) via download_geoguessr_poi_imagery.py.
  • Optical: multispectral optical COGs from a public STAC catalog, blue/green/red or visual preview, percentile-stretched to uint8.
  • Labels: per-pixel land-cover raster from Earth Engine, reprojected to the same 10 m grid as the RGB stack, then tiled and nearest-neighbor downsampled with the mask.
  • Context: optional geographic overhead still per POI (token-based static map API) under mapbox_stills/.
  • Rows: global caption + grounding per tile of satellite imagery, plus per–land-cover-class caption (and optional grounding) when a class exceeds --min-class-fraction.
  • Downstream format: messages[] with image + text, aligned with refs/satellite-vlm / VRSBench converter conventions.

Layout

  • images/*.png — RGB chips (downsampled, e.g. 224×224).
  • overlays/*.png — optional bbox visualization per JSONL row (--write-bbox-overlays).
  • mapbox_stills/*.png — overhead context still per POI (when API token set).
  • data/train.jsonl, data/validation.jsonl, data/test.jsonl — splits hashed by poi_id.
  • metadata/*.json — per-tile sidecars (coordinates, scene id, land_cover_label_meta counts/shapes only).

Build

See docs/scripts/SPEC-lfm-vl-sft-dataset.md and python data/scripts/build_lfm_vl_sft_dataset.py --help.

Licenses

Respect the STAC data provider terms, Earth Engine terms for label rasters, the static map provider ToS for optional stills, and the MIT HF pano dataset license for source coordinates.