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license: cc-by-nc-sa-4.0
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# LISAt_PRE

**LISAt_PRE** is a remote-sensing-focused MLLM that is tailored to improve performance in scenarios requiring detailed visual understanding and natural language reasoning over satellite and aerial imagery.

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## Overview

LISAt_PRE enhances the [LISAt](https://huggingface.co/jquenum/LISAt-7b) framework by adapting it to remote-sensing applications, which require better handling of diverse visual data and specialized query types. The architecture integrates:

- A **Remote-CLIP ViT-L/14** vision encoder
- A **Vicuna-7B** LLM for text understanding and reasoning
- A **linear projection module** to align vision and language representations
- A segmentation model trained on high-quality mask annotations

An architectural overview is shown in Figure 3 (refer to paper).

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## Key Features

- **Remote-Sensing Specialization**: Trained on domain-specific imagery to handle the unique challenges of satellite data.
- **Multimodal Alignment**: Combines textual and visual inputs through a unified architecture.
- **Training with [PreGRES](https://huggingface.co/datasets/jquenum/PreGRES/blob/main/README.md)**: LISAt_PRE is pre-trained on the [PreGRES](https://huggingface.co/datasets/jquenum/PreGRES/blob/main/README.md) dataset using LoRA (Hu et al., 2021), before being fine-tuned on GRES.

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## Architecture

- **Language Model**: Vicuna-7B (Chiang et al., 2023)
- **Vision Encoder**: Remote-CLIP ViT-L/14 (Liu et al., 2024a)

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## Citation

If you use LISAt_PRE in your work, please cite:

```bibtex
@article{quenum2025lisat,
  title={LISAt: Language-Instructed Segmentation Assistant for Satellite Imagery},
  author={Quenum, Jerome and Hsieh, Wen-Han and Wu, Tsung-Han and Gupta, Ritwik and Darrell, Trevor and Chan, David M},
  journal={arXiv preprint arXiv:2505.02829},
  year={2025},
  url={https://arxiv.org/pdf/2505.02829}
}