CGEarthEye:A High-Resolution Remote Sensing Vision Foundation Model Based on the Jilin-1 Satellite Constellation
Zhiwei Yi, Xin Cheng, Jingyu Ma, Ruifei Zhu, Junwei Tian, Yuanxiu Zhou, Xinge Zhao, Hongzhe Li

TL;DR
CGEarthEye is a high-resolution remote sensing foundation model based on the Jilin-1 satellite constellation, utilizing self-supervised learning and multiple backbones to achieve state-of-the-art performance across various Earth Observation tasks.
Contribution
The paper introduces CGEarthEye, the first RSVFM tailored for Jilin-1 data, with a novel multi-temporal SSL dataset and multiple pre-training strategies, advancing high-resolution remote sensing modeling.
Findings
Achieves SOTA performance on 10 benchmark datasets
Demonstrates superior feature visualization and model convergence
Enhances practical mapping applications with high-resolution data
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…
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Taxonomy
TopicsRemote-Sensing Image Classification · Remote Sensing in Agriculture · Advanced Neural Network Applications
