RSVLM SFT: Remote Sensing Vision-Language Model Training Data

MF-RSVLM is a remote sensing vision-language model (VLM) combining a CLIP vision encoder and a Vicuna-7B language model. The model was trained in two stages for modality alignment and instruction following. The dataset is associated with the FUSE-RSVLM project and was uploaded by RL-MIND.

Use Cases

• Training vision-language models for remote sensing based on the described two-stage training process.
• Aligning visual and textual modalities for satellite imagery based on the CLIP encoder and MLP projector architecture.
• Instruction-tuning large language models for geospatial tasks based on the Vicuna-7B LLM component.

Strengths

• Model architecture is explicitly described, combining CLIP, a two-layer MLP projector, and Vicuna-7B.
• Training methodology is specified as a two-stage process for modality alignment and instruction following.

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

RL-MIND

Collection Method

Likely created for training the MF-RSVLM/FUSE-RSVLM model, but specific data collection method is not detailed.

Freshness

Last updated 2026-01-09 02:57:32; freshness should be verified.

Geography

Platform tags suggest a 'us' region, but specific spatial coverage is unknown.