The specific place identification based on remote sensing images is of great significance in smart city planning, land use analysis, and safe city construction. However, it is difficult to identify these places with simple rules by using traditional classification or target recognition methods due to the complex environmental background. Convolutional neural networks have strong spatial information mining capabilities. In this article, we improved the well-known YOLOv3 model to a new model called YOLO-S-CIoU for school place identification. The main improvements include:1) Using the SRXnet module to replace the Darknet53 module in YOLOv3 to improve the feature learning ability; 2) Using complete-IoU loss (CIoU loss) to optimize the regression of the bounding box; 3) Training and verification based on self-made school place sample dataset (SS dataset). The results showed that the average accuracy (AP) of YOLO-S-CIoU reached 96.46%; the parameter amount was 226 MB. Compared with YOLOv3, YOLO-S-CIoU has achieved a 9 MB reduction in parameter volume and a 2.3% increase in AP. In addition, in the regional remote sensing images of Tumshuk and Yantai, the recall rates of school places were increased by 37.5% and 42.2% than YOLOv3, respectively. These works show that the improved model has stronger robustness and higher identification ability in remote sensing image identification in different areas.
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