An Improved Geospatial Object Detection Framework for Complex Urban and Environmental Remote Sensing Scenes

The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detection framework named RS-YOLO is introduced by adopting the YOLOv11 architecture. The model integrates Dynamic Convolution for adaptive receptive field adjustment, Selective Kernel Attention for multi-path feature aggregation, and the MPDIoU loss function for geometry-aware localization. The proposed approach outperforms in experimental results on the TGRS-HRRSD dataset of 13 scenes from different geospatial scenarios, giving an 89.0% mAP and an 87 F1-score. Beyond algorithmic advancement, RS-YOLO provides a GeoAI-based analytical tool for applications such as urban infrastructure monitoring, land use management, and transportation facility recognition, enabling spatially informed and sustainable decision-making in complex remote sensing environments.