RSGPT: A Remote Sensing Vision Language Model and Benchmark (2307.15266v1)

Abstract: The emergence of large-scale LLMs, with GPT-4 as a prominent example, has significantly propelled the rapid advancement of artificial general intelligence and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing (RS), there is a growing interest in developing large vision LLMs (VLMs) specifically tailored for data analysis in this domain. However, current research predominantly revolves around visual recognition tasks, lacking comprehensive, large-scale image-text datasets that are aligned and suitable for training large VLMs, which poses significant challenges to effectively training such models for RS applications. In computer vision, recent research has demonstrated that fine-tuning large vision LLMs on small-scale, high-quality datasets can yield impressive performance in visual and language understanding. These results are comparable to state-of-the-art VLMs trained from scratch on massive amounts of data, such as GPT-4. Inspired by this captivating idea, in this work, we build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the RS field. Unlike previous RS datasets that either employ model-generated captions or short descriptions, RSICap comprises 2,585 human-annotated captions with rich and high-quality information. This dataset offers detailed descriptions for each image, encompassing scene descriptions (e.g., residential area, airport, or farmland) as well as object information (e.g., color, shape, quantity, absolute position, etc). To facilitate the evaluation of VLMs in the field of RS, we also provide a benchmark evaluation dataset called RSIEval. This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.

Overview of "RSGPT: A Remote Sensing Vision LLM and Benchmark"

The paper focuses on advancing the capabilities of vision-LLMs (VLMs) in the remote sensing domain. The authors present RSGPT, a novel large-scale vision LLM specifically designed for remote sensing applications. The core innovation of this paper lies in overcoming the current limitations regarding datasets suitable for training such models.

Development of Remote Sensing Data Sets

To enable the efficient training of vision-LLMs in remote sensing, the authors developed a high-quality Remote Sensing Image Captioning dataset (RSICap) and an evaluation database called RSIEval. Unlike existing datasets, RSICap contains 2,585 human-annotated captions, providing detailed scene descriptions, object information, and visual reasoning insights. RSIEval, with its inclusion of image-captions and visual question-answer pairs, extends evaluation beyond image captioning to include diverse tasks, facilitating a comprehensive benchmarking process for VLMs in this domain.

Architectural and Methodological Contributions

RSGPT marks a methodological shift by focusing on adjusting only specific parts of the pre-trained models for domain-specific applications, thereby enhancing data efficiency. Leveraging existing pre-trained VLMs, the paper maximizes performance gains using the RSICap dataset to fine-tune the models effectively. In particular, only the Q-Former network and a linear layer on InstructBLIP are fine-tuned, which ensures that the alignment of visual and textual features is precise and computationally feasible.

Experimental Validation

The paper presents extensive experimental validations demonstrating the prowess of RSGPT compared to state-of-the-art models across different tasks, including remote sensing image captioning and visual question answering. The RSGPT model shows superior capabilities in understanding and generating detailed descriptions and answering complex VQA tasks with higher accuracy and fewer errors compared to models like BLIP-2 and MiniGPT-4. On established datasets such as UCM-captions, Sydney-captions, and RSIEval, RSGPT outperformed other existing methods in most metrics by a significant margin, showcasing its applicability and robustness.

Implications and Future Directions

This research poses a substantial advancement in the application of AI to remote sensing tasks and suggests further exploring the integration of multimodal transformers in spatial reasoning and complex scene interpretation. The development of the RSICap and RSIEval corpora provides an essential resource, potentially driving future research focused on fine-grained scene understanding, object detection, and semantic segmentation in remote sensing imagery.

Furthermore, this paper opens avenues for applying similar methodologies in other vastly different domains lacking large-scale aligned datasets, tipping the balance towards domain-specific language-vision understanding even with limited data resources. The future offerings could include expanded datasets that incorporate even larger scales of data, more diverse geolocations, or inclusion of temporal changes for broader applicability in real-world scenarios.

In summary, this paper constitutes an important step toward advancing vision-LLMs tailored for remote sensing, emphasizing the potential of finely-tuned, high-quality datasets to significantly enhance model performance across complex multi-modal tasks.