# Aquila: A Hierarchically Aligned Visual-Language Model for Enhanced Remote Sensing Image Comprehension

arXiv: https://arxiv.org/abs/2411.06074 · HTML: https://mlsanity.com/paper/2411.06074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [CogVLM: Visual Expert for Pretrained Language Models](https://mlsanity.com/paper/2311.03079/) (2023)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [Towards Vision-Language Geo-Foundation Model: A Survey](https://mlsanity.com/paper/2406.09385/) (2024)

## Built on by (most cited first, 1 of 1)

- [SPEX: A Vision-Language Model for Land Cover Extraction on Spectral Remote Sensing Images](https://mlsanity.com/paper/2508.05202/) (2025)

## Abstract (arXiv)

Recently, large vision language models (VLMs) have made significant strides in visual language capabilities through visual instruction tuning, showing great promise in the field of remote sensing image interpretation. However, existing remote sensing vision language models (RSVLMs) often fall short in capturing the complex characteristics of remote sensing scenes, as they typically rely on low resolution, single scale visual features and simplistic methods to map visual features to language features. In this paper, we present Aquila, an advanced visual language foundation model designed to enable richer visual feature representation and more precise visual-language feature alignment for remote sensing images. Our approach introduces a learnable Hierarchical Spatial Feature Integration (SFI) module that supports high resolution image inputs and aggregates multi scale visual features, allowing for the detailed representation of complex visual information. Additionally, the SFI module is repeatedly integrated into the layers of the large language model (LLM) to achieve deep visual language feature alignment, without compromising the model's performance in natural language processing tasks. These innovations, capturing detailed visual effects through higher resolution and multi scale input, and enhancing feature alignment significantly improve the model's ability to learn from image text data. We validate the effectiveness of Aquila through extensive quantitative experiments and qualitative analyses, demonstrating its superior performance.