NuTonic/sat-vl-sft-training-ready-v1

Dataset Summary

NuTonic/sat-bbox-metadata-sft-v1 is a metadata-first, procedural VLM SFT dataset built from an existing “sat-bbox” style dataset tree (Sentinel‑2 chips + per-tile JSON metadata sidecars, optionally paired Mapbox stills).

The goal is to create high-signal, production-shaped supervision for multimodal chat models:

• Captioning for satellite chips
• Grounding (bounding boxes in normalized coordinates) for land-cover regions
• Class-focused captions and absence checks for specific land-cover classes
• Cross-view reasoning using optional Mapbox overhead context
• Production-like analytical summaries that include:
  • Sentinel‑2 imagery
  • An additional procedural “analysis image” (a TiM-like predicted-class raster)
  • A compact TiM-shaped analytics JSON block
  • A profile-specific assistant summary (land use change, wildfire, flood pulse, etc.)

This dataset is generated without calling Mapbox APIs; it only uses paths that already exist in the input dataset root.

What “procedural” means here

The dataset uses deterministic, rule-based / synthetic construction to:

• transform metadata sidecars into multiple supervised tasks,
• produce a TiM-shaped analytics JSON structure,
• render an additional analysis PNG per row (for the analytical tasks).

These rows are intended for instruction tuning and format/behavior alignment, not as ground-truth scientific measurements.

Land-cover classes

Land-cover semantics follow Google Dynamic World v1 class ids (0–8):

id	label
0	water
1	trees
2	grass
3	flooded_vegetation
4	crops
5	shrub_and_scrub
6	built
7	bare_ground
8	snow_and_ice

Data format (what’s in data/*.jsonl)

Rows are JSON objects with a messages list in a chat format compatible with common VLM SFT pipelines.

Each row is one conversation like:

• system (optional; used for production-analysis rows)
• user: one or more {"type":"image","image":"<relative path>"} parts, followed by one {"type":"text","text":"..."} part
• assistant: {"type":"text","text":"..."}

Some rows also include a metadata object on the top-level row (not to be confused with the dataset metadata/ folder). For example, production-analysis rows include:

• metadata.sample_id
• metadata.task
• metadata.analysis_profile
• metadata.tile_stem
• metadata.split
• metadata.image_paths
• metadata.analysis_image_path

Tasks emitted

Depending on build configuration, the builder can emit the following task types:

• production_analysis:
  • 2–3 images (Sentinel‑2 + optional Mapbox + generated analysis image)
  • user prompt contains a compact TiM-shaped JSON block
  • assistant output is an application-specific analytical summary
• caption:
  • 1 Sentinel‑2 image
  • assistant caption derived from sidecar
• grounding_all:
  • 1 Sentinel‑2 image
  • assistant output is JSON list of boxes: [{"label": str, "bbox":[x1,y1,x2,y2]}, ...] with coords normalized to 0–1
• grounding_per_class:
  • same as grounding, but per dominant class label
• class_focus:
  • 1 Sentinel‑2 image
  • assistant describes only a specific class’ approximate share/layout
• absence:
  • 1 Sentinel‑2 image
  • assistant answers conservatively about whether a class is substantively present
• cross_view (optional; only if mapbox still paths exist):
  • 2 images: Mapbox still + Sentinel‑2 chip
  • assistant relates overhead context to satellite/labels

Repository layout (files on the Hub)

A typical exported dataset root contains:

• data/
  • train.jsonl
  • validation.jsonl
  • test.jsonl
• images/...
  Satellite chips (copied or hardlinked from the source tree)
• mapbox_stills/... (optional)
  Only included if present in the source dataset and enabled in the build config
• analysis_images/...
  Generated procedural PNGs used by production_analysis rows
• metadata/
  • sft_metadata_rows/
    • *.json one sidecar per emitted SFT row (build provenance + specs used)

metadata/sft_metadata_rows/*.json (row sidecars)

For each JSONL row, a sidecar is written under metadata/sft_metadata_rows/ containing the fields used to build that row. Example fields vary by task but commonly include:

• sample_id
• task
• tile_stem
• split
• image_paths

For production_analysis, sidecars also include:

• analysis_profile
• analysis_image_path
• analysis_image_spec (the serializable spec used to render the PNG)
• sentinel_sidecar (a cleaned observation extracted from the original per-tile metadata)

How this dataset is created

This dataset is built from an input dataset root that contains:

• data/*.jsonl with stable relative image paths such as:
  • images/.../<tile_stem>.png
  • optionally mapbox_stills/.../<poi_id>.png
• metadata/**/*.json per-tile sidecars containing at minimum:
  • tile_stem
  • poi_id (preferred) and/or split
  • caption (for caption tasks)
  • class_fractions (for analysis + some derived tasks)
  • regions (for grounding tasks; pixel coords in the model output resolution)

The builder walks metadata/**/*.json, joins to data/*.jsonl by tile_stem, then emits multiple SFT tasks per tile depending on configuration.

Intended use

• Instruction tuning / SFT for multimodal chat models that accept a list of messages with interleaved images.
• Learning stable formatting for:
  • captions,
  • grounding JSON outputs,
  • profile-specific “analyst” summaries.

Not intended for:

• precise geospatial measurement,
• scientific change detection validation,
• legal or operational maritime detection claims.

Known limitations / considerations

• Many targets are procedural (synthetic TiM-shaped signals and templated summaries).
• Grounding boxes are derived from metadata sidecars (not from human annotation in this builder).
• Cross-view reasoning depends on whether mapbox_stills/ paths exist in the input dataset.
• Prompts are filtered to prevent accidental training on large internal blobs (certain substrings are banned).

License

This repository does not define a universal license for all upstream imagery/metadata in the source dataset tree. Please ensure you have the rights to redistribute the underlying source content used to build this dataset.

Citation

If you use this dataset, please cite the dataset repo and the upstream sources it was derived from (your internal sat-bbox dataset tree and any imagery providers).

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Hub layout (sharded)

This snapshot was processed with python data/scripts/shard_lfm_vl_dataset_for_hub.py so that images/, mapbox_stills/, overlays/, analysis_images/, metadata/, and metadata/sft_metadata_rows/ use at most 9000 files per leaf directory (Hub git limit: 10k files per directory). JSONL paths may include sNNNNN/ shard segments where needed.

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Hub layout (sharded)

This snapshot was processed with python data/scripts/shard_lfm_vl_dataset_for_hub.py so that images/, mapbox_stills/, overlays/, analysis_images/, metadata/, and metadata/sft_metadata_rows/ use at most 9000 files per leaf directory (Hub git limit: 10k files per directory). JSONL paths may include sNNNNN/ shard segments where needed.