## 1 Introduction  
### Remote-Sensing Big Data  
As satellite remote-sensing (RS) technology has developed, the spatial and temporal resolution of satellite images has significantly improved over the past two decades. The intelligent applications of remote-sensing big data, therefore, are bound to be researched and developed, which aim to provide scientific big data and decision-making assistance for supporting sustainable social and economic development while building a community with a shared future for humankind [1]. Spatial information is of essentially importance for the sustainable administration of government, while remote-sensing big data are able to be used in the investigation and monitoring of national territory resources and security because they are characterized by wide coverage, high resolution and user privacy [2]. Moreover, remote-sensing big data could also effectively alleviate the difficulties of manual information collection and acquisition and promote the updating and integrating of background information. Finally, the combination of high-resolution remote-sensing images, global navigation and positioning (GPS) technology and geographic information systems (GISs) could implement the efficient reconnaissance and dynamic trajectory analysis of criminal activity, especially in sparsely populated regions, the west of China, for example. When [PERSON], former vice president of the United States, proposed \"big data for the earth\", satellite remote sensing was entering the era of big data [3]. Since the successful launch of the first US meteorological satellite in 1960, there have been 558 earth observation satellites launched, as of September 2020 [4]. China, for instance, has more than 500 satellites in orbit, as of 24 April 2022. How remote-sensing data can be accurately, fast and intelligently gathered and how the information coming from remote-sensing satellite big data can be mined are the core issues with the application of big data of remote-sensing satellites [5]. In the past decade, remote-sensing big data platforms, such as the Google Earth Engine (GEE) [6], NEX (NASA Earth exchange), Descartes Labs, Amazon Web Services (AWS), Data Cube of Australia and CODE-DE (Copernicus Data and Exploitation Platform-DE) from Germany, etc., have rapidly developed and achieved many typical applications [7; 8; 9]. China's remote-sensing cloud-computing platform is also rapidly developing. For example, the Earth Data Miner [10] supported by the pilot project \"Earth Big Data Science Project\", run by the Chinese Academy of Sciences, and Pixel Information Expert (PIE), a cloud computation platform of PIESAT Group Inc., are representative remote sensing big data platforms in early development. In addition, Alibaba cloud, Tencent cloud and the Sense Earth platforms of SenseTime Group Inc. have also play a leading role in terms of remote-sensing cloud computing in China. Remote-sensing cloud-computing platforms make the real-time processing of massive satellite remote-sensing big data possible and have been industrialized in many fields, such as earth science, GISs, disaster prevention and mitigation, emergency rescue and even public security. With the advent of the era of big data, deep learning models represented by decision trees, random forests, convolutional neural networks, etc. have been widely used in the field of remote sensing. A large number of remote-sensing data sets has been produced and released, such as UC Merced, WHU-RS19, Gaofen Image Datasets (GIDs), etc., over the past few years, and those data sets have been successfully applied in neural net optimization and prediction algorithm improvement [11]. As deep learning models require massive samples to be trained and tested, the demand for high-resolution remotely sensed image data sets has exploded, which has been enhanced by the rapid development of the big data of remote sensing, parallel computing and artificial intelligence.