Luojia-AI: A Full-Stack Cloud Computing Infrastructure for Remote Sensing Intellignet Interpretation

Zhan Zhang, Mi Zhang, Jianya Gong, Xiangyun Hu, Liangcun Jiang, Huan Zhou, Jiansi Yang

Abstract: The rapid processing, analysis, and mining of remote sensing big data using intelligent interpretation technology on remote sensing cloud computing platforms (RS-CCPs) have emerged as a new trend. However, existing RS-CCPs primarily focus on optimizing data storage and intelligent computing for common visual representation, overlooking key characteristics of remote sensing data such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding. This oversight hinders computational efficiency and accuracy in remote sensing image interpretation. To address this, we have developed the LuoJia-AI platform, comprising the LuoJiaSET standard large-scale sample database and the dedicated deep learning framework, LuoJiaNET. This platform achieves state-of-the-art performance on five crucial remote sensing interpretation tasks: scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. LuoJia-AI bridges the gap between the sample database and the deep learning framework, exhibiting significant potential for high-precision remote sensing mapping applications.