This research introduces the first large-scale multimodal dialogue dataset for synthetic aperture radar (SAR) image interpretation, enabling AI systems to understand and analyze all-weather Earth observation data.

Key Innovations:

• Bridges the gap between powerful Vision-Language Models (VLMs) and specialized SAR remote sensing applications
• Enables critical military reconnaissance and surveillance capabilities through AI-assisted interpretation
• Provides a benchmark for evaluating AI performance in professional domains requiring specialized expertise
• Supports security operations with all-weather Earth observation capabilities

Security Implications: This advancement allows security organizations to leverage AI for interpreting SAR imagery in military reconnaissance, maritime surveillance, and infrastructure monitoring without being limited by weather conditions.

SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image Interpretation