Evaluating Tool-Augmented Agents in Remote Sensing Platforms

arXiv:2405.00709v1 Announce Type: new
Abstract: Tool-augmented Large Language Models (LLMs) have shown impressive capabilities in remote sensing (RS) applications. However, existing benchmarks assume question-answering input templates over predefined image-text data pairs. These standalone instructions neglect the intricacies of realistic user-grounded tasks. Consider a geospatial analyst: they zoom in a map area, they draw a region over which to collect satellite imagery, and they succinctly ask "Detect all objects here". Where is `here`, if it is not explicitly hardcoded in the …

abstract agents analyst applications arxiv benchmarks capabilities cs.ai cs.cl cs.lg data geospatial however image language language models large language large language models llms map platforms question sensing tasks text tool type zoom