LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation

ABSTRACT

The rapid processing, analysis, and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have recently become a new trend. The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation, which ignores remote sensing data characteristics such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding, thus impairing computational efficiency and accuracy. We construct a LuoJiaAI platform composed of a standard large-scale sample database (LuoJiaSET) and a dedicated deep learning framework (LuoJiaNET) to achieve state-of-the-art performance on five typical remote sensing interpretation tasks, including scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application. In addition, LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium (OGC) standards for better developing and sharing Earth Artificial Intelligence (AI) algorithms and products on benchmark datasets. LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism, showing great potential in high-precision remote sensing mapping applications.

KEYWORDS:

• Artificial intelligence
• cloud computing platform
• remote-sensing intelligent interpretation
• sample database
• deep learning framework

Acknowledgments

We appreciate the effort led by WHU-LuoJiaAI Group and Huawei Artificial Intelligence Group. Great thanks to all the colleagues who have provided their time and advice while writing this paper. Special thanks to Dr. Liangcun Jiang, Dr. Yue Xu, Mr. Haowei Jia, Mr. Yuanxin Zhao, Ms. Siqi Liu, Mr. Bingnan Yang, Mr. Zhenzhang Yang, Mr. Teng Su, Mr. Guowen Zhang, Mr. Laiping Ding, Mr. Shenkai Zhoung, Mr. Jingjun Wang, Mr. Kunyang Tian, Pro. Peng Yue, Pro. Shunping Ji, Pro. Xin Huang, Pro. Haigang Sui, Pro. Guisong Xia, Pro. Yansheng Li, Pro. Kaimin Sun, Pro. Juhua Liu, Pro. Mengting Yuan, Dr. Jiayi Li, and Dr. Xinyu Wang for all the kind support. Thanks to the rest of the WHU-LuoJiaAI Group: Mr. Bin Wang, Ms. Jin Liu, Mr. Haotian Teng, Mr. Jianxun Wang, Mr. Wangbin Li, Mr. Lilin Tu, Mr. Ning Zhou, Mr. Hengwei Zhao, Mr. Yang Pan, Mr. Wei Chen. Finally, we would like to thank the providers of the thousands of public datasets in LuoJiaAI.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available on GitHub at https://github.com/WHULuoJiaTeam.

Additional information

Funding

This work is supported by the Chinese National Natural Science Foundation Projects [grant number 41901265], a Major Program of the National Natural Science Foundation of China [grant number 92038301], and was supported in part by the Special Fund of Hubei Luojia Laboratory [grant number 220100028].

Notes on contributors

Zhan Zhang

Zhan Zhang is a PhD student at the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan, China. His research interests mainly include machine learning system, remote sensing intelligent interpretation, and machine vision. He contributed to the LuoJiaAI platform design and integration, LuoJiaNET’s remote sensing characteristics module implementation, and manuscript writing of this paper.

Mi Zhang

Mi Zhang is an associate researcher at the School of Remote Sensing and Information Engineering, Wuhan University. He serves as the chief artificial intelligence scientist in Handleray Corporation and technical director in WHU-LuoJiaAI Group. His research interests mainly include computer vision, machine learning, with particular interest in semantic object segmentation and the construction of DL framework. He contributed to the overall architecture design and implementation of the LuoJiaNET, and manuscript writing of this paper.

Jianya Gong

Jianya Gong is the Academician of Chinese Academy of Sciences, Professor and Dean of School of Remote Sensing and Information Engineering, Wuhan University. His research interests mainly include remote sensing image processing, spatial data infrastructure, geospatial data interoperability, and artificial intelligence. He contributed to the LuoJiaAI project initiation, and implementation of this paper.

Xiangyun Hu

Xiangyun Hu is a professor and head of the department of photogrammetry with the School of Remote Sensing and Information Engineering, Wuhan University. His research interests mainly include artificial intelligence and pattern recognition, remote sensing software development, and remote sensing intelligent interpretation. He led the WHU-LuoJiaAI Group and contributed to the methodology and implementation of this paper.

Hanjiang Xiong

Hanjiang Xiong is a professor in 3-D geographic information system at Wuhan University. His research interests mainly include geospatial data management, 3-D visualization, augmented reality, and indoor and outdoor geographic information system. He contributed to the implementation of this paper.

Huan Zhou

Huan Zhou is a master’s student at the Department of Land-Surveying and Geo-Informatics, Hong Kong Polytechnic University. His research interest is in web server development and cartographic visualization. He contributed to the overall architecture design and implementation of the LuoJiaNET’s front-end layer, and use cases of this paper.

Zhipeng Cao

Zhipeng Cao is a PhD student at the School of Remote Sensing and Information Engineering, Wuhan University. His research interest is in big spatiotemporal data management. He contributed to the implementation of this paper.