Osco, L.P.; Lemos, E.L. de; Gonçalves, W.N.; Ramos, A.P.M.; Junior, J.M. The Potential of Visual ChatGPT For Remote Sensing. Remote Sensing 2023, 15, 3232, doi:10.3390/rs15133232.
Osco, L.P.; Lemos, E.L. de; Gonçalves, W.N.; Ramos, A.P.M.; Junior, J.M. The Potential of Visual ChatGPT For Remote Sensing. Remote Sensing 2023, 15, 3232, doi:10.3390/rs15133232.
Osco, L.P.; Lemos, E.L. de; Gonçalves, W.N.; Ramos, A.P.M.; Junior, J.M. The Potential of Visual ChatGPT For Remote Sensing. Remote Sensing 2023, 15, 3232, doi:10.3390/rs15133232.
Osco, L.P.; Lemos, E.L. de; Gonçalves, W.N.; Ramos, A.P.M.; Junior, J.M. The Potential of Visual ChatGPT For Remote Sensing. Remote Sensing 2023, 15, 3232, doi:10.3390/rs15133232.
Abstract
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.
Keywords
artificial intelligence; image analysis; visual language model
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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