This paper presents a method for object recognition and automatic labeling in large-area remote sensing images called LRSAA. The method integrates YOLOv11 and MobileNetV3-SSD object detection algorithms through ensemble learning to enhance model performance. Furthermore, it employs Poisson disk sampling segmentation techniques and the EIOU metric to optimize the training and inference processes of segmented images, followed by the integration of results. This approach not only reduces the demand for computational resources but also achieves a good balance between accuracy and speed. The source code for this project has been made publicly available on https://github.com/anaerovane/LRSAA.

翻译：本文提出了一种用于大面积遥感图像目标识别与自动标注的方法，称为LRSAA。该方法通过集成学习融合YOLOv11与MobileNetV3-SSD目标检测算法以提升模型性能。此外，采用泊松圆盘采样分割技术与EIOU度量优化分割图像的训练与推理流程，继而整合结果。该方法不仅降低了对计算资源的需求，同时在精度与速度之间取得了良好平衡。本项目源代码已公开于https://github.com/anaerovane/LRSAA。