  Recent advancements in Natural Language Processing (NLP), particularly in
Large Language Models (LLMs), associated with deep learning-based computer
vision techniques, have shown substantial potential for automating a variety of
tasks. One notable model is Visual ChatGPT, which combines ChatGPT's LLM
capabilities with visual computation to enable effective image analysis. The
model's ability to process images based on textual inputs can revolutionize
diverse fields. However, its application in the remote sensing domain remains
unexplored. This is the first paper to examine the potential of Visual ChatGPT,
a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of
image processing related to the remote sensing domain. Among its current
capabilities, Visual ChatGPT can generate textual descriptions of images,
perform canny edge and straight line detection, and conduct image segmentation.
These offer valuable insights into image content and facilitate the
interpretation and extraction of information. By exploring the applicability of
these techniques within publicly available datasets of satellite images, we
demonstrate the current model's limitations in dealing with remote sensing
images, highlighting its challenges and future prospects. Although still in
early development, we believe that the combination of LLMs and visual models
holds a significant potential to transform remote sensing image processing,
creating accessible and practical application opportunities in the field.
