Unleashing the Potential of Remote Sensing Foundation Models via Bridging Data and Computility Islands
Abstract
The rapid advancement of Earth observation (EO) capabilities is driving an explosive increase in remote sensing data. There is an urgent need for advanced processing techniques to unleash their application value. Generalist EO intelligence refers to the ability to provide unified support for qualitative interpretation, quantitative inversion, and interactive dialogue across diverse EO data and tasks. It has attracted significant attention recently, prompting academia, industry, and government to invest substantial resources. Through developing remote sensing foundation models (RSFMs), generalist EO intelligence can ultimately offer humanity a shared spatial-temporal intelligence service in various fields (e.g., agriculture, forestry, and oceanography). However, a critical question remains: have we truly unleashed the potential of RSFMs for generalist EO intelligence? Despite the vast volume of remote sensing data, their distribution is often fragmented and decentralized due to privacy concerns, storage bottlenecks, industrial competition, and geo-information security. This fragmentation leads to data islands, which limit the full utilization of multi-source remote sensing data. Moreover, computility (i.e., computational resources) typically develops in isolation, inadequately supporting the large-scale training and application of RSFMs.
