High precision control points are indispensable for the improvement of geometric positioning accuracy of aerial and space images. At present, most control points need to be installed manually, and which obtained in this way are fixed to a specific area and have high installation and maintenance cost. Satellites can only correct their orbit and attitude in real time when they pass through the area with control points. Therefore, setting up control points by this way has poor flexibility and is not conducive to the improvement of satellite positioning accuracy. In order to solve this problem, an automatic control point generation algorithm based on natural ground object automatic recognition and detection is proposed. First, typical ground objects such as playground and road intersection are automatically identified by YOLO algorithm, and feature extraction is carried out by classic SIFT feature extraction operator on the basis of recognition. Then, the feature extraction results, along with the target attribute, location and other information are stored in the agreed format. Finally, the data of control points are organized by the multi-scale integer coding method based on quadruplication to improve the efficiency of data storage and access. This method can make full use of high precision surveying and mapping satellite image data and set up control points around the world. Satellites can correct their orbit and attitude at any time according to their needs, and can greatly improve the positioning accuracy of images.