  Remote Sensing Large Multi-Modal Models (RSLMMs) are developing rapidly and
showcase significant capabilities in remote sensing imagery (RSI)
comprehension. However, due to the limitations of existing datasets, RSLMMs
have shortcomings in understanding the rich semantic relations among objects in
complex remote sensing scenes. To unlock RSLMMs' complex comprehension ability,
we propose a large-scale instruction tuning dataset FIT-RS, containing
1,800,851 instruction samples. FIT-RS covers common interpretation tasks and
innovatively introduces several complex comprehension tasks of escalating
difficulty, ranging from relation reasoning to image-level scene graph
generation. Based on FIT-RS, we build the FIT-RSFG benchmark. Furthermore, we
establish a new benchmark to evaluate the fine-grained relation comprehension
capabilities of LMMs, named FIT-RSRC. Based on combined instruction data, we
propose SkySenseGPT, which achieves outstanding performance on both public
datasets and FIT-RSFG, surpassing existing RSLMMs. We hope the FIT-RS dataset
can enhance the relation comprehension capability of RSLMMs and provide a
large-scale fine-grained data source for the remote sensing community. The
dataset will be available at https://github.com/Luo-Z13/SkySenseGPT
