A Vision Centric Remote Sensing Benchmark

Understand

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored.

• Unlike natural images, RS imagery presents unique challenges that current MLLMs struggle to handle, particularly in visual grounding and spatial reasoning.
• This study investigates the limitations of CLIP-based MLLMs in RS, highlighting their failure to differentiate visually distinct yet semantically similar RS images.
• To address this, we introduce a remote sensing multimodal visual patterns (RSMMVP) benchmark.