Satellite imagery and aligned land-cover outputs packaged as image–text rows for fine-tuning in SFT format. The dataset, authored by NuTonic, was last updated on 2026-04-23. JSONL user prompts name the modality where it matters.
Use Cases
- Fine-tuning vision-language models based on satellite imagery and text prompts.
- Training models for land-cover classification based on aligned label outputs.
- Developing models for geospatial question-answering based on the multimodal SFT format.
- Conducting research in remote sensing based on Sentinel-2 optical data and land-cover labels.
Strengths
- Includes aligned land-cover outputs for satellite imagery, providing structured training pairs.
- Uses Sentinel-2 multispectral optical data from a public STAC catalog, suggesting a reliable source.
- Data is formatted for supervised fine-tuning (SFT), indicating a specific use-case orientation.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- Sentinel 2 multispectral optical COGs from a public STAC catalog.
- Collection Method
- Satellite imagery and labels packaged as image-text rows.
- Time Range
- null
- Freshness
- Last updated 2026-04-23 12:57:03; freshness should be verified.
- Geography
- Locations include GeoGuessr-style POIs, but specific regions are not detailed.