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

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.

1. INTRODUCTION

With the continuous enrichment of remote sensing (RS) images and the remarkable success of artificial intelligence (AI) in computer vision, the field of RS image interpretation is urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operations, and reducing costs in image processing and analysis workflow [1], [2]. In recent years, a wide variety of remote sensing cloud computing platforms (RS-CCPs), including Google Earth Engine [3], NASA Earth Exchange [4], Geoscience Data Cube [5], Descartes Labs [6], CASEarth EarthDataMiner [7], and PIE-Engine [8], have been developed for large-scale geospatial data management, processing, and analysis. These RS-CCPs provide large-scale geospatial data management, processing, and analysis capabilities. By combining vast amounts of earth observation data with high-performance cloud computing services, they have overcome the limitations of traditional desktop-based RS platforms. As a result, RS-CCPs have been widely adopted in various geoscience applications [9]–[11].