Rapid advancements in large language models for quantitative remote sensing: The case of water depth inversion

Abstract

This study presents a comparative analysis of two advanced AI models, ChatGPT and ERNIE, in the context of water depth inversion. Utilizing satellite spectral data and in-situ bathymetric measurements collected from Rushikonda Beach, India, we processed and analyzed the data to generate high-resolution bathymetric maps. Both models demonstrated significant accuracy, with ChatGPT slightly outperforming ERNIE in terms of mean absolute error. The study highlights the advantages of AI models, such as efficient data processing and the ability to integrate multi-modal inputs, while also discussing challenges related to data quality, interpretability, and computational demands. The findings suggest that while both models are highly effective for water depth inversion, ongoing improvements in data handling and model transparency are essential for their broader application in environmental monitoring. This research contributes to the understanding of AI capabilities in geospatial analysis and sets the stage for future enhancements in the field.