  The remote sensing image intelligence understanding model is undergoing a new
profound paradigm shift which has been promoted by multi-modal large language
model (MLLM), i.e. from the paradigm learning a domain model (LaDM) shifts to
paradigm learning a pre-trained general foundation model followed by an
adaptive domain model (LaGD). Under the new LaGD paradigm, the old datasets,
which have led to advances in RSI intelligence understanding in the last
decade, are no longer suitable for fire-new tasks. We argued that a new dataset
must be designed to lighten tasks with the following features: 1)
Generalization: training model to learn shared knowledge among tasks and to
adapt to different tasks; 2) Understanding complex scenes: training model to
understand the fine-grained attribute of the objects of interest, and to be
able to describe the scene with natural language; 3) Reasoning: training model
to be able to realize high-level visual reasoning. In this paper, we designed a
high-quality, diversified, and unified multimodal instruction-following dataset
for RSI understanding produced by GPT-4V and existing datasets, which we called
RS-GPT4V. To achieve generalization, we used a (Question, Answer) which was
deduced from GPT-4V via instruction-following to unify the tasks such as
captioning and localization; To achieve complex scene, we proposed a
hierarchical instruction description with local strategy in which the
fine-grained attributes of the objects and their spatial relationships are
described and global strategy in which all the local information are integrated
to yield detailed instruction descript; To achieve reasoning, we designed
multiple-turn QA pair to provide the reasoning ability for a model. The
empirical results show that the fine-tuned MLLMs by RS-GPT4V can describe
fine-grained information. The dataset is available at:
https://github.com/GeoX-Lab/RS-GPT4V.
