CGEarthEye:A High-Resolution Remote Sensing Vision Foundation Model Based on the Jilin-1 Satellite Constellation
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Deep learning methods have significantly advanced the development of
intelligent rinterpretation in remote sensing (RS), with foundational model
research based on large-scale pre-training paradigms rapidly reshaping various
domains of Earth Observation (EO). However, compared to the open accessibility
and high spatiotemporal coverage of medium-resolution data, the limited
acquisition channels for ultra-high-resolution optical RS imagery have
constrained the progress of high-resolution remote sensing vision foundation
models (RSVFM). As the world's largest sub-meter-level commercial RS satellite
constellation, the Jilin-1 constellation possesses abundant sub-meter-level
image resources. This study proposes CGEarthEye, a RSVFM framework specifically
designed for Jilin-1 satellite characteristics, comprising five backbones with
different parameter scales with totaling 2.1 billion parameters. To enhance the
representational capacity of the foundation model, we developed JLSSD, the
first 15-million-scale multi-temporal self-supervised learning (SSL) dataset
featuring global coverage with quarterly temporal sampling within a single
year, constructed through multi-level representation clustering and sampling
strategies. The framework integrates seasonal contrast, augmentation-based
contrast, and masked patch token contrastive strategies for pre-training.
Comprehensive evaluations across 10 benchmark datasets covering four typical RS
tasks demonstrate that the CGEarthEye consistently achieves state-of-the-art
(SOTA) performance. Further analysis reveals CGEarthEye's superior
characteristics in feature visualization, model convergence, parameter
efficiency, and practical mapping applications. This study anticipates that the
exceptional representation capabilities of CGEarthEye will facilitate broader
and more efficient applications of Jilin-1 data in traditional EO application.
