Generative AI for Earth Observation, a Prospect - Mohammadi, Mohammad Reza; Omid, Ghozatlou; Nazir, Muhammad Saqib; Keymasi, Mobina; Iqbal, Muhammad Amjad; Mihai Datcu (My University, 2025)
The huge amount of Earth Observation (EO) data from satellites and airborne platforms provides immense opportunities and new challenges for extracting real-time and precise information. Artificial Intelligence (AI) and Deep Learning (DL) have revolutionized how we analyze and process EO data. More specifically, Generative AI (GenAI) has already transformed many EO applications and this transformation is accelerating rapidly with the advancement of GenAI. Various generative models have been developed and applied to different EO applications, including synthetic data generation, gap filling, and super-resolution. Comprehensively understanding this new paradigm is necessary to envision the prospect of GenAI for different EO applications, its potential, limitations, and future impact. The main objective of this study is to provide a clearer image of the current state of GenAI in EO through a critical analysis of three different GenAI models, and to present a realistic forward-looking view on how GenAI could impact EO data processing in the future.
Keywords: Earth; Deep learning; Generative AI; Image synthesis; Image color analysis; Superresolution; Transforms; Data processing; Data models; Synthetic data