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 ro…
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