  Recent advances in visual prompting in the natural image area have allowed
users to interact with artificial intelligence (AI) tools through various
visual marks such as box, point, and free-form shapes. However, due to the
significant difference between the natural and remote sensing (RS) images,
existing visual prompting models face challenges in RS scenarios. Moreover, RS
MLLMs mainly focus on interpreting image-level RS data and only support
interaction with language instruction, restricting flexibility applications in
the real world. To address those limitations, the first visual prompting model
named EarthMarker is proposed, which excels in image-level, region-level, and
point-level RS imagery interpretation. Specifically, the visual prompts
alongside images and text instruction input into the large language model
(LLM), adapt models toward specific predictions and tasks. Subsequently, a
sharing visual encoding method is introduced to refine multi-scale image
features and visual prompt information uniformly. Furthermore, to endow the
EarthMarker with versatile multi-granularity visual perception abilities, the
cross-domain phased learning strategy is developed, and the disjoint parameters
are optimized in a lightweight manner by leveraging both the natural and RS
domain-specific knowledge. In addition, to tackle the lack of RS visual
prompting data, a dataset named RSVP featuring multi-modal fine-grained visual
prompting instruction is constructed. Extensive experiments are conducted to
demonstrate the proposed EarthMarker's competitive performance, representing a
significant advance in multi-granularity RS imagery interpretation under the
visual prompting learning framework.
